%0 Journal Article %@ 2564-1891 %I JMIR Publications %V 5 %N %P e67119 %T Understanding Interventions to Address Infodemics Through Epidemiological, Socioecological, and Environmental Health Models: Framework Analysis %A John,Jennifer N %A Gorman,Sara %A Scales,David %K infodemics %K misinformation %K disinformation %K Covid-19 %K infodemic management %K health communication %K pandemic preparedness %D 2025 %7 24.3.2025 %9 %J JMIR Infodemiology %G English %X Background: The COVID-19 pandemic was accompanied by a barrage of false, misleading, and manipulated information that inhibited effective pandemic response and led to thousands of preventable deaths. Recognition of the urgent public health threat posed by this infodemic led to the development of numerous infodemic management interventions by a wide range of actors. The need to respond rapidly and with limited information sometimes came at the expense of strategy and conceptual rigor. Given limited funding for public health communication and growing politicization of countermisinformation efforts, responses to future infodemics should be informed by a systematic and conceptually grounded evaluation of the successes and shortcomings of existing interventions to ensure credibility of the field and evidence-based action. Objectives: This study sought to identify gaps and opportunities in existing infodemic management interventions and to assess the use of public health frameworks to structure responses to infodemics. Methods: We expanded a previously developed dataset of infodemic management interventions, spanning guidelines, policies, and tools from governments, academic institutions, nonprofits, media companies, and other organizations, with 379 interventions included in total. We applied framework analysis to describe and interpret patterns within these interventions through their alignment with codes derived from 3 frameworks selected for their prominence in public health and infodemic-related scholarly discourse: the epidemiological model, the socioecological model, and the environmental health framework. Results: The epidemiological model revealed the need for rigorous, transparent risk assessments to triage misinformation. The socioecological model demonstrated an opportunity for greater coordination across levels of influence, with only 11% of interventions receiving multiple socioecological codes, and more robust partnerships with existing organizations. The environmental health framework showed that sustained approaches that comprehensively address all influences on the information environment are needed, representing only 19% of the dataset. Conclusions: Responses to future infodemics would benefit from cross-sector coordination, adoption of measurable and meaningful goals, and alignment with public health frameworks, which provide critical conceptual grounding for infodemic response approaches and ensure comprehensiveness of approach. Beyond individual interventions, a funded coordination mechanism can provide overarching strategic direction and promote collaboration. %R 10.2196/67119 %U https://infodemiology.jmir.org/2025/1/e67119 %U https://doi.org/10.2196/67119 %0 Journal Article %@ 2564-1891 %I JMIR Publications %V 5 %N %P e66524 %T Experiences of Public Health Professionals Regarding Crisis Communication During the COVID-19 Pandemic: Systematic Review of Qualitative Studies %A Okuhara,Tsuyoshi %A Terada,Marina %A Okada,Hiroko %A Yokota,Rie %A Kiuchi,Takahiro %+ Department of Health Communication, The University of Tokyo, 7 Chome-3-1 Hongo, Tokyo, 113-8654, Japan, 81 3 5800 6549, okuhara-ctr@umin.ac.jp %K COVID-19 %K health communication %K infodemic %K misinformation %K social media %K SARS-CoV-2 %K pandemic %K infectious %K digital age %K systematic review %K internet %K public health %K government %K health professional %K crisis communication %K qualitative %K disinformation %K eHealth %K digital health %K medical informatics %D 2025 %7 14.3.2025 %9 Review %J JMIR Infodemiology %G English %X Background: The COVID-19 pandemic emerged in the digital age and has been called the first “data-driven pandemic” in human history. The global response demonstrated that many countries had failed to effectively prepare for such an event. Learning through experience in a crisis is one way to improve the crisis management process. As the world has returned to normal after the pandemic, questions about crisis management have been raised in several countries and require careful consideration. Objective: This review aimed to collect and organize public health professionals’ experiences in crisis communication to the public during the COVID-19 pandemic. Methods: We searched PubMed, MEDLINE, CINAHL, Web of Science, Academic Search Complete, PsycINFO, PsycARTICLES, and Communication Abstracts in February 2024 to locate English-language articles that qualitatively investigated the difficulties and needs experienced by health professionals in their communication activities during the COVID-19 pandemic. Results: This review included 17 studies. Our analysis identified 7 themes and 20 subthemes. The 7 themes were difficulties in pandemic communication, difficulties caused by the “infodemic,” difficulties in partnerships within or outside of public health, difficulties in community engagement, difficulties in effective communication, burnout among communicators, and the need to train communication specialists and establish a permanent organization specializing in communication. Conclusions: This review identified the gaps between existing crisis communication guidelines and real-world crisis communication in the digital environment and clarified the difficulties and needs that arose from these gaps. Crisis communication strategies and guidelines should be updated with reference to the themes revealed in this review to effectively respond to subsequent public health crises. Trial Registration: PROSPERO CRD42024528975; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=528975 International Registered Report Identifier (IRRID): RR2-10.2196/58040 %M 40085849 %R 10.2196/66524 %U https://infodemiology.jmir.org/2025/1/e66524 %U https://doi.org/10.2196/66524 %U http://www.ncbi.nlm.nih.gov/pubmed/40085849 %0 Journal Article %@ 2564-1891 %I JMIR Publications %V 5 %N %P e58539 %T Geosocial Media’s Early Warning Capabilities Across US County-Level Political Clusters: Observational Study %A Arifi,Dorian %A Resch,Bernd %A Santillana,Mauricio %A Guan,Weihe Wendy %A Knoblauch,Steffen %A Lautenbach,Sven %A Jaenisch,Thomas %A Morales,Ivonne %A Havas,Clemens %+ Department of Geoinformatics, University of Salzburg, Kapitelgasse 4/6, Salzburg, 5020, Austria, 43 662 80440, dorian.arifi@plus.ac.at %K spatiotemporal epidemiology %K geo-social media data %K digital disease surveillance %K political polarization %K epidemiological early warning %K digital early warning %D 2025 %7 30.1.2025 %9 Original Paper %J JMIR Infodemiology %G English %X Background: The novel coronavirus disease (COVID-19) sparked significant health concerns worldwide, prompting policy makers and health care experts to implement nonpharmaceutical public health interventions, such as stay-at-home orders and mask mandates, to slow the spread of the virus. While these interventions proved essential in controlling transmission, they also caused substantial economic and societal costs and should therefore be used strategically, particularly when disease activity is on the rise. In this context, geosocial media posts (posts with an explicit georeference) have been shown to provide a promising tool for anticipating moments of potential health care crises. However, previous studies on the early warning capabilities of geosocial media data have largely been constrained by coarse spatial resolutions or short temporal scopes, with limited understanding of how local political beliefs may influence these capabilities. Objective: This study aimed to assess how the epidemiological early warning capabilities of geosocial media posts for COVID-19 vary over time and across US counties with differing political beliefs. Methods: We classified US counties into 3 political clusters, democrat, republican, and swing counties, based on voting data from the last 6 federal election cycles. In these clusters, we analyzed the early warning capabilities of geosocial media posts across 6 consecutive COVID-19 waves (February 2020-April 2022). We specifically examined the temporal lag between geosocial media signals and surges in COVID-19 cases, measuring both the number of days by which the geosocial media signals preceded the surges in COVID-19 cases (temporal lag) and the correlation between their respective time series. Results: The early warning capabilities of geosocial media data differed across political clusters and COVID-19 waves. On average, geosocial media posts preceded COVID-19 cases by 21 days in republican counties compared with 14.6 days in democrat counties and 24.2 days in swing counties. In general, geosocial media posts were preceding COVID-19 cases in 5 out of 6 waves across all political clusters. However, we observed a decrease over time in the number of days that posts preceded COVID-19 cases, particularly in democrat and republican counties. Furthermore, a decline in signal strength and the impact of trending topics presented challenges for the reliability of the early warning signals. Conclusions: This study provides valuable insights into the strengths and limitations of geosocial media data as an epidemiological early warning tool, particularly highlighting how they can change across county-level political clusters. Thus, these findings indicate that future geosocial media based epidemiological early warning systems might benefit from accounting for political beliefs. In addition, the impact of declining geosocial media signal strength over time and the role of trending topics for signal reliability in early warning systems need to be assessed in future research. %M 39883923 %R 10.2196/58539 %U https://infodemiology.jmir.org/2025/1/e58539 %U https://doi.org/10.2196/58539 %U http://www.ncbi.nlm.nih.gov/pubmed/39883923 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 27 %N %P e59786 %T US State Public Health Agencies' Use of Twitter From 2012 to 2022: Observational Study %A Mendez,Samuel R %A Munoz-Najar,Sebastian %A Emmons,Karen M %A Viswanath,Kasisomayajula %+ Department of Social and Behavioral Sciences, Harvard T.H. Chan School of Public Health, 677 Huntington Ave, Boston, MA, 02115, United States, 1 617 432 1135, smendez@g.harvard.edu %K social media %K health communication %K Twitter %K tweet %K public health %K state government %K government agencies %K information technology %K data science %K communication tool %K COVID-19 pandemic %K data collection %K theoretical framework %K message %K interaction %D 2025 %7 3.1.2025 %9 Original Paper %J J Med Internet Res %G English %X Background: Twitter (subsequently rebranded as X) is acknowledged by US health agencies, including the US Centers for Disease Control and Prevention (CDC), as an important public health communication tool. However, there is a lack of data describing its use by state health agencies over time. This knowledge is important amid a changing social media landscape in the wake of the COVID-19 pandemic. Objective: The study aimed to describe US state health agencies’ use of Twitter from 2012 through 2022. Furthermore, we organized our data collection and analysis around the theoretical framework of the networked public to contribute to the broader literature on health communication beyond a single platform. Methods: We used Twitter application programming interface data as indicators of state health agencies’ engagement with the 4 key qualities of communication in a networked public: scalability, persistence, replicability, and searchability. To assess scalability, we calculated tweet volume and audience engagement metrics per tweet. To assess persistence, we calculated the portion of tweets that were manual retweets or included an account mention. To assess replicability, we calculated the portion of tweets that were retweets or quote tweets. To assess searchability, we calculated the portion of tweets using at least 1 hashtag. Results: We observed a COVID-19 pandemic–era shift in state health agency engagement with scalability. The overall volume of tweets increased suddenly from less than 50,000 tweets in 2019 to over 94,000 in 2020, resulting in an average of 5.3 per day. Though mean tweets per day fell in 2021 and 2022, this COVID-19 pandemic–era low was still higher than the pre–COVID-19 pandemic peak. We also observed a more fragmented approach to searchability aligning with the start of the COVID-19 pandemic. More state-specific hashtags were among the top 10 during the COVID-19 pandemic, compared with more general hashtags related to disease outbreaks and natural disasters in years before. We did not observe such a clear COVID-19 pandemic–era shift in engagement with replicability. The portion of tweets mentioning a CDC account gradually rose and fell around a peak of 7.0% in 2018. Similarly, the rate of retweets of a CDC account rose and fell gradually around a peak of 5.4% in 2018. We did not observe a clear COVID-19 pandemic–era shift in persistence. The portion of tweets mentioning any account reached a maximum of 21% in 2013. It oscillated for much of the study period before dropping off in 2021 and reaching a minimum of 10% in 2022. Before 2018, the top 10 mentioned accounts included at least 2 non-CDC or corporate accounts. From 2018 onward, state agencies were much more prominent. Conclusions: Overall, we observed a more fragmented approach to state health agency communication on Twitter during the pandemic, prioritizing volume over searchability, formally replicating existing messages, and leaving traces of interactions with other accounts. %M 39752190 %R 10.2196/59786 %U https://www.jmir.org/2025/1/e59786 %U https://doi.org/10.2196/59786 %U http://www.ncbi.nlm.nih.gov/pubmed/39752190 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 10 %N %P e51909 %T Infodemics and Vaccine Confidence: Protocol for Social Listening and Insight Generation to Inform Action %A Kolis,Jessica %A Brookmeyer,Kathryn %A Chuvileva,Yulia %A Voegeli,Christopher %A Juma,Sarina %A Ishizumi,Atsuyoshi %A Renfro,Katy %A Wilhelm,Elisabeth %A Tice,Hannah %A Fogarty,Hannah %A Kocer,Irma %A Helms,Jordan %A Verma,Anisha %+ Global Immunization Division, Global Health Center, Centers for Disease Control and Prevention, 1600 Clifton Rd, Atlanta, GA, 30333, United States, 1 404 718 3876, ywe5@cdc.gov %K infodemic %K infodemic management %K vaccine confidence %K vaccine demand %K misinformation %K disinformation %K infodemiology %K mixed methods %K thematic analysis %K COVID-19 %D 2024 %7 24.10.2024 %9 Protocol %J JMIR Public Health Surveill %G English %X Background: In the fall of 2020, the COVID-19 infodemic began to affect public confidence in and demand for COVID-19 vaccines in the United States. While polls indicated what consumers felt regarding COVID-19 vaccines, they did not provide an understanding of why they felt that way or the social and informational influences that factored into vaccine confidence and uptake. It was essential for us to better understand how information ecosystems were affecting the confidence in and demand for COVID-19 vaccines in the United States. Objective: The US Centers for Disease Control and Prevention (CDC) established an Insights Unit within the COVID-19 Response’s Vaccine Task Force in January 2021 to assist the agency in acting more swiftly to address the questions, concerns, perceptions, and misinformation that appeared to be affecting uptake of COVID-19 vaccines. We established a novel methodology to rapidly detect and report on trends in vaccine confidence and demand to guide communication efforts and improve programmatic quality in near real time. Methods: We identified and assessed data sources for inclusion through an informal landscape analysis using a snowball method. Selected data sources provided an expansive look at the information ecosystem of the United States regarding COVID-19 vaccines. The CDC’s Vaccinate with Confidence framework and the World Health Organization’s behavioral and social drivers for vaccine decision-making framework were selected as guiding principles for interpreting generated insights and their impact. We used qualitative thematic analysis methods and a consensus-building approach to identify prevailing and emerging themes, assess their potential threat to vaccine confidence, and propose actions to increase confidence and demand. Results: As of August 2022, we have produced and distributed 34 reports to >950 recipients within the CDC and externally. State and local health departments, nonprofit organizations, professional associations, and congressional committees have referenced and used the reports for learning about COVID-19 vaccine confidence and demand, developing communication strategies, and demonstrating how the CDC monitored and responded to misinformation. A survey of the reports’ end users found that nearly 75% (40/53) of respondents found them “very” or “extremely” relevant and 52% (32/61) used the reports to inform communication strategies. In addition, our methodology underwent continuous process improvement to increase the rigor of the research process, the validity of the findings, and the usability of the reports. Conclusions: This methodology can serve as a diagnostic technique for rapidly identifying opportunities for public health interventions and prevention. As the methodology itself is adaptable, it could be leveraged and scaled for use in a variety of public health settings. Furthermore, it could be considered beyond acute public health crises to support adherence to guidance and recommendations and could be considered within routine monitoring and surveillance systems. %M 39447166 %R 10.2196/51909 %U https://publichealth.jmir.org/2024/1/e51909 %U https://doi.org/10.2196/51909 %U http://www.ncbi.nlm.nih.gov/pubmed/39447166 %0 Journal Article %@ 2564-1891 %I JMIR Publications %V 4 %N %P e60678 %T Evaluating the Influence of Role-Playing Prompts on ChatGPT’s Misinformation Detection Accuracy: Quantitative Study %A Haupt,Michael Robert %A Yang,Luning %A Purnat,Tina %A Mackey,Tim %+ Global Health Program, Department of Anthropology, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA, 92093, United States, 1 858 534 4145, tkmackey@ucsd.edu %K large language models %K ChatGPT %K artificial intelligence %K AI %K experiment %K prompt engineering %K role-playing %K social identity %K misinformation detection %K COVID-19 %D 2024 %7 26.9.2024 %9 Original Paper %J JMIR Infodemiology %G English %X Background: During the COVID-19 pandemic, the rapid spread of misinformation on social media created significant public health challenges. Large language models (LLMs), pretrained on extensive textual data, have shown potential in detecting misinformation, but their performance can be influenced by factors such as prompt engineering (ie, modifying LLM requests to assess changes in output). One form of prompt engineering is role-playing, where, upon request, OpenAI’s ChatGPT imitates specific social roles or identities. This research examines how ChatGPT’s accuracy in detecting COVID-19–related misinformation is affected when it is assigned social identities in the request prompt. Understanding how LLMs respond to different identity cues can inform messaging campaigns, ensuring effective use in public health communications. Objective: This study investigates the impact of role-playing prompts on ChatGPT’s accuracy in detecting misinformation. This study also assesses differences in performance when misinformation is explicitly stated versus implied, based on contextual knowledge, and examines the reasoning given by ChatGPT for classification decisions. Methods: Overall, 36 real-world tweets about COVID-19 collected in September 2021 were categorized into misinformation, sentiment (opinions aligned vs unaligned with public health guidelines), corrections, and neutral reporting. ChatGPT was tested with prompts incorporating different combinations of multiple social identities (ie, political beliefs, education levels, locality, religiosity, and personality traits), resulting in 51,840 runs. Two control conditions were used to compare results: prompts with no identities and those including only political identity. Results: The findings reveal that including social identities in prompts reduces average detection accuracy, with a notable drop from 68.1% (SD 41.2%; no identities) to 29.3% (SD 31.6%; all identities included). Prompts with only political identity resulted in the lowest accuracy (19.2%, SD 29.2%). ChatGPT was also able to distinguish between sentiments expressing opinions not aligned with public health guidelines from misinformation making declarative statements. There were no consistent differences in performance between explicit and implicit misinformation requiring contextual knowledge. While the findings show that the inclusion of identities decreased detection accuracy, it remains uncertain whether ChatGPT adopts views aligned with social identities: when assigned a conservative identity, ChatGPT identified misinformation with nearly the same accuracy as it did when assigned a liberal identity. While political identity was mentioned most frequently in ChatGPT’s explanations for its classification decisions, the rationales for classifications were inconsistent across study conditions, and contradictory explanations were provided in some instances. Conclusions: These results indicate that ChatGPT’s ability to classify misinformation is negatively impacted when role-playing social identities, highlighting the complexity of integrating human biases and perspectives in LLMs. This points to the need for human oversight in the use of LLMs for misinformation detection. Further research is needed to understand how LLMs weigh social identities in prompt-based tasks and explore their application in different cultural contexts. %M 39326035 %R 10.2196/60678 %U https://infodemiology.jmir.org/2024/1/e60678 %U https://doi.org/10.2196/60678 %U http://www.ncbi.nlm.nih.gov/pubmed/39326035 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 26 %N %P e38786 %T Prevalence of Health Misinformation on Social Media—Challenges and Mitigation Before, During, and Beyond the COVID-19 Pandemic: Scoping Literature Review %A Kbaier,Dhouha %A Kane,Annemarie %A McJury,Mark %A Kenny,Ian %+ School of Computing and Communications, The Open University, Walton Hall, Milton Keynes, MK7 6AA, United Kingdom, dhouha.kbaier@open.ac.uk %K health misinformation %K online health communities %K vaccine hesitancy %K social media %K health professionals %K public health %K COVID-19 %K intervention %K antivaxxers %D 2024 %7 19.8.2024 %9 Review %J J Med Internet Res %G English %X Background: This scoping review accompanies our research study “The Experience of Health Professionals With Misinformation and Its Impact on Their Job Practice: Qualitative Interview Study.” It surveys online health misinformation and is intended to provide an understanding of the communication context in which health professionals must operate. Objective: Our objective was to illustrate the impact of social media in introducing additional sources of misinformation that impact health practitioners’ ability to communicate effectively with their patients. In addition, we considered how the level of knowledge of practitioners mitigated the effect of misinformation and additional stress factors associated with dealing with outbreaks, such as the COVID-19 pandemic, that affect communication with patients. Methods: This study used a 5-step scoping review methodology following Arksey and O’Malley’s methodology to map relevant literature published in English between January 2012 and March 2024, focusing on health misinformation on social media platforms. We defined health misinformation as a false or misleading health-related claim that is not based on valid evidence or scientific knowledge. Electronic searches were performed on PubMed, Scopus, Web of Science, and Google Scholar. We included studies on the extent and impact of health misinformation in social media, mitigation strategies, and health practitioners’ experiences of confronting health misinformation. Our independent reviewers identified relevant articles for data extraction. Results: Our review synthesized findings from 70 sources on online health misinformation. It revealed a consensus regarding the significant problem of health misinformation disseminated on social network platforms. While users seek trustworthy sources of health information, they often lack adequate health and digital literacies, which is exacerbated by social and economic inequalities. Cultural contexts influence the reception of such misinformation, and health practitioners may be vulnerable, too. The effectiveness of online mitigation strategies like user correction and automatic detection are complicated by malicious actors and politicization. The role of health practitioners in this context is a challenging one. Although they are still best placed to combat health misinformation, this review identified stressors that create barriers to their abilities to do this well. Investment in health information management at local and global levels could enhance their capacity for effective communication with patients. Conclusions: This scoping review underscores the significance of addressing online health misinformation, particularly in the postpandemic era. It highlights the necessity for a collaborative global interdisciplinary effort to ensure equitable access to accurate health information, thereby empowering health practitioners to effectively combat the impact of online health misinformation. Academic research will need to be disseminated into the public domain in a way that is accessible to the public. Without equipping populations with health and digital literacies, the prevalence of online health misinformation will continue to pose a threat to global public health efforts. %M 39159456 %R 10.2196/38786 %U https://www.jmir.org/2024/1/e38786 %U https://doi.org/10.2196/38786 %U http://www.ncbi.nlm.nih.gov/pubmed/39159456 %0 Journal Article %@ 2564-1891 %I JMIR Publications %V 4 %N %P e50125 %T Collective Intelligence–Based Participatory COVID-19 Surveillance in Accra, Ghana: Pilot Mixed Methods Study %A Marley,Gifty %A Dako-Gyeke,Phyllis %A Nepal,Prajwol %A Rajgopal,Rohini %A Koko,Evelyn %A Chen,Elizabeth %A Nuamah,Kwabena %A Osei,Kingsley %A Hofkirchner,Hubertus %A Marks,Michael %A Tucker,Joseph D %A Eggo,Rosalind %A Ampofo,William %A Sylvia,Sean %+ Department of Health Policy and Management, University of North Carolina, 1101D McGavran-Greenberg Hall, CB #7411 Chapel Hill, NC 27599-7411, Chapel Hill, NC, 27599, United States, 1 919 966 6328, sysylvia@email.unc.edu %K information markets %K participatory disease surveillance %K collective intelligence %K community engagement %K the wisdom of the crowds %K Ghana %K mobile phone %D 2024 %7 12.8.2024 %9 Original Paper %J JMIR Infodemiology %G English %X Background: Infectious disease surveillance is difficult in many low- and middle-income countries. Information market (IM)–based participatory surveillance is a crowdsourcing method that encourages individuals to actively report health symptoms and observed trends by trading web-based virtual “stocks” with payoffs tied to a future event. Objective: This study aims to assess the feasibility and acceptability of a tailored IM surveillance system to monitor population-level COVID-19 outcomes in Accra, Ghana. Methods: We designed and evaluated a prediction markets IM system from October to December 2021 using a mixed methods study approach. Health care workers and community volunteers aged ≥18 years living in Accra participated in the pilot trading. Participants received 10,000 virtual credits to trade on 12 questions on COVID-19–related outcomes. Payoffs were tied to the cost estimation of new and cumulative cases in the region (Greater Accra) and nationwide (Ghana) at specified future time points. Questions included the number of new COVID-19 cases, the number of people likely to get the COVID-19 vaccination, and the total number of COVID-19 cases in Ghana by the end of the year. Phone credits were awarded based on the tally of virtual credits left and the participant’s percentile ranking. Data collected included age, occupation, and trading frequency. In-depth interviews explored the reasons and factors associated with participants’ user journey experience, barriers to system use, and willingness to use IM systems in the future. Trading frequency was assessed using trend analysis, and ordinary least squares regression analysis was conducted to determine the factors associated with trading at least once. Results: Of the 105 eligible participants invited, 21 (84%) traded at least once on the platform. Questions estimating the national-level number of COVID-19 cases received 13 to 19 trades, and obtaining COVID-19–related information mainly from television and radio was associated with less likelihood of trading (marginal effect: −0.184). Individuals aged <30 years traded 7.5 times more and earned GH ¢134.1 (US $11.7) more in rewards than those aged >30 years (marginal effect: 0.0135). Implementing the IM surveillance was feasible; all 21 participants who traded found using IM for COVID-19 surveillance acceptable. Active trading by friends with communal discussion and a strong onboarding process facilitated participation. The lack of bidirectional communication on social media and technical difficulties were key barriers. Conclusions: Using an IM system for disease surveillance is feasible and acceptable in Ghana. This approach shows promise as a cost-effective source of information on disease trends in low- and middle-income countries where surveillance is underdeveloped, but further studies are needed to optimize its use. %M 39133907 %R 10.2196/50125 %U https://infodemiology.jmir.org/2024/1/e50125 %U https://doi.org/10.2196/50125 %U http://www.ncbi.nlm.nih.gov/pubmed/39133907 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 8 %N %P e48284 %T Effects of Intervention Timing on Health-Related Fake News: Simulation Study %A Gwon,Nahyun %A Jeong,Wonjeong %A Kim,Jee Hyun %A Oh,Kyoung Hee %A Jun,Jae Kwan %+ Cancer Knowledge and Information Center, National Cancer Control Institute, National Cancer Center, 323 Ilsan-ro, Ilsandong-gu, Goyang, 10408, Republic of Korea, 82 31 920 2184, jkjun@ncc.re.kr %K disinformation %K fenbendazole %K cancer information %K simulation %K fake news %K online social networking %K misinformation %K lung cancer %D 2024 %7 7.8.2024 %9 Original Paper %J JMIR Form Res %G English %X Background: Fake health-related news has spread rapidly through the internet, causing harm to individuals and society. Despite interventions, a fenbendazole scandal recently spread among patients with lung cancer in South Korea. It is crucial to intervene appropriately to prevent the spread of fake news. Objective: This study investigated the appropriate timing of interventions to minimize the side effects of fake news. Methods: A simulation was conducted using the susceptible-infected-recovered (SIR) model, which is a representative model of the virus spread mechanism. We applied this model to the fake news spread mechanism. The parameters were set similarly to those in the digital environment, where the fenbendazole scandal occurred. NetLogo, an agent-based model, was used as the analytical tool. Results: Fake news lasted 278 days in the absence of interventions. As a result of adjusting and analyzing the timing of the intervention in response to the fenbendazole scandal, we found that faster intervention leads to a shorter duration of fake news (intervention at 54 days = fake news that lasted for 210 days; intervention at 16 days = fake news that lasted for 187 days; and intervention at 10 days = fake news that lasted for 157 days). However, no significant differences were observed when the intervention was performed within 10 days. Conclusions: Interventions implemented within 10 days were effective in reducing the duration of the spread of fake news. Our findings suggest that timely intervention is critical for preventing the spread of fake news in the digital environment. Additionally, a monitoring system that can detect fake news should be developed for a rapid response %M 39109788 %R 10.2196/48284 %U https://formative.jmir.org/2024/1/e48284 %U https://doi.org/10.2196/48284 %U http://www.ncbi.nlm.nih.gov/pubmed/39109788 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 12 %N %P e50048 %T Toward Fairness, Accountability, Transparency, and Ethics in AI for Social Media and Health Care: Scoping Review %A Singhal,Aditya %A Neveditsin,Nikita %A Tanveer,Hasnaat %A Mago,Vijay %+ Department of Mathematics and Computing Science, Saint Mary's University, 923 Robie Street, Halifax, NS, B3H 3C3, Canada, 1 902 420 5893, Nikita.Neveditsin@smu.ca %K fairness, accountability, transparency, and ethics %K artificial intelligence %K social media %K health care %D 2024 %7 3.4.2024 %9 Review %J JMIR Med Inform %G English %X Background: The use of social media for disseminating health care information has become increasingly prevalent, making the expanding role of artificial intelligence (AI) and machine learning in this process both significant and inevitable. This development raises numerous ethical concerns. This study explored the ethical use of AI and machine learning in the context of health care information on social media platforms (SMPs). It critically examined these technologies from the perspectives of fairness, accountability, transparency, and ethics (FATE), emphasizing computational and methodological approaches that ensure their responsible application. Objective: This study aims to identify, compare, and synthesize existing solutions that address the components of FATE in AI applications in health care on SMPs. Through an in-depth exploration of computational methods, approaches, and evaluation metrics used in various initiatives, we sought to elucidate the current state of the art and identify existing gaps. Furthermore, we assessed the strength of the evidence supporting each identified solution and discussed the implications of our findings for future research and practice. In doing so, we made a unique contribution to the field by highlighting areas that require further exploration and innovation. Methods: Our research methodology involved a comprehensive literature search across PubMed, Web of Science, and Google Scholar. We used strategic searches through specific filters to identify relevant research papers published since 2012 focusing on the intersection and union of different literature sets. The inclusion criteria were centered on studies that primarily addressed FATE in health care discussions on SMPs; those presenting empirical results; and those covering definitions, computational methods, approaches, and evaluation metrics. Results: Our findings present a nuanced breakdown of the FATE principles, aligning them where applicable with the American Medical Informatics Association ethical guidelines. By dividing these principles into dedicated sections, we detailed specific computational methods and conceptual approaches tailored to enforcing FATE in AI-driven health care on SMPs. This segmentation facilitated a deeper understanding of the intricate relationship among the FATE principles and highlighted the practical challenges encountered in their application. It underscored the pioneering contributions of our study to the discourse on ethical AI in health care on SMPs, emphasizing the complex interplay and the limitations faced in implementing these principles effectively. Conclusions: Despite the existence of diverse approaches and metrics to address FATE issues in AI for health care on SMPs, challenges persist. The application of these approaches often intersects with additional ethical considerations, occasionally leading to conflicts. Our review highlights the lack of a unified, comprehensive solution for fully and effectively integrating FATE principles in this domain. This gap necessitates careful consideration of the ethical trade-offs involved in deploying existing methods and underscores the need for ongoing research. %M 38568737 %R 10.2196/50048 %U https://medinform.jmir.org/2024/1/e50048 %U https://doi.org/10.2196/50048 %U http://www.ncbi.nlm.nih.gov/pubmed/38568737 %0 Journal Article %@ 2564-1891 %I JMIR Publications %V 4 %N %P e49699 %T Exploring the Impact of the COVID-19 Pandemic on Twitter in Japan: Qualitative Analysis of Disrupted Plans and Consequences %A Kamba,Masaru %A She,Wan Jou %A Ferawati,Kiki %A Wakamiya,Shoko %A Aramaki,Eiji %+ Division of Information Science, Graduate School of Science and Technology, Nara Institute of Science and Technology, 8916-5 Takayama-cho, Ikoma, 630-0192, Japan, 81 0743 72 5250, aramaki@is.naist.jp %K COVID-19 %K natural language processing %K NLP %K Twitter %K disrupted plans %K concerns %D 2024 %7 1.4.2024 %9 Original Paper %J JMIR Infodemiology %G English %X Background: Despite being a pandemic, the impact of the spread of COVID-19 extends beyond public health, influencing areas such as the economy, education, work style, and social relationships. Research studies that document public opinions and estimate the long-term potential impact after the pandemic can be of value to the field. Objective: This study aims to uncover and track concerns in Japan throughout the COVID-19 pandemic by analyzing Japanese individuals’ self-disclosure of disruptions to their life plans on social media. This approach offers alternative evidence for identifying concerns that may require further attention for individuals living in Japan. Methods: We extracted 300,778 tweets using the query phrase Corona-no-sei (“due to COVID-19,” “because of COVID-19,” or “considering COVID-19”), enabling us to identify the activities and life plans disrupted by the pandemic. The correlation between the number of tweets and COVID-19 cases was analyzed, along with an examination of frequently co-occurring words. Results: The top 20 nouns, verbs, and noun plus verb pairs co-occurring with Corona no-sei were extracted. The top 5 keywords were graduation ceremony, cancel, school, work, and event. The top 5 verbs were disappear, go, rest, can go, and end. Our findings indicate that education emerged as the top concern when the Japanese government announced the first state of emergency. We also observed a sudden surge in anxiety about material shortages such as toilet paper. As the pandemic persisted and more states of emergency were declared, we noticed a shift toward long-term concerns, including careers, social relationships, and education. Conclusions: Our study incorporated machine learning techniques for disease monitoring through the use of tweet data, allowing the identification of underlying concerns (eg, disrupted education and work conditions) throughout the 3 stages of Japanese government emergency announcements. The comparison with COVID-19 case numbers provides valuable insights into the short- and long-term societal impacts, emphasizing the importance of considering citizens’ perspectives in policy-making and supporting those affected by the pandemic, particularly in the context of Japanese government decision-making. %M 38557446 %R 10.2196/49699 %U https://infodemiology.jmir.org/2024/1/e49699 %U https://doi.org/10.2196/49699 %U http://www.ncbi.nlm.nih.gov/pubmed/38557446 %0 Journal Article %@ 2564-1891 %I JMIR Publications %V 4 %N %P e47699 %T The Journey of Engaging With Web-Based Self-Harm and Suicide Content: Longitudinal Qualitative Study %A Haime,Zoë %A Kennedy,Laura %A Grace,Lydia %A Cohen,Rachel %A Derges,Jane %A Biddle,Lucy %+ Population Health Sciences, Bristol Medical School, University of Bristol, Canynge Hall, 39 Whatley Road, Bristol, BS8 2PS, United Kingdom, 44 01179289000, zoe.haime@bristol.ac.uk %K suicide %K self-harm %K online %K longitudinal %K qualitative %D 2024 %7 28.3.2024 %9 Original Paper %J JMIR Infodemiology %G English %X Background: Self-harm and suicide are major public health concerns worldwide, with attention focused on the web environment as a helpful or harmful influence. Longitudinal research on self-harm and suicide–related internet use is limited, highlighting a paucity of evidence on long-term patterns and effects of engaging with such content. Objective: This study explores the experiences of people engaging with self-harm or suicide content over a 6-month period. Methods: This study used qualitative and digital ethnographic methods longitudinally, including one-to-one interviews at 3 time points to explore individual narratives. A trajectory analysis approach involving 4 steps was used to interpret the data. Results: The findings from 14 participants established the web-based journey of people who engage with self-harm or suicide content. In total, 5 themes were identified: initial interactions with self-harm or suicide content, changes in what self-harm or suicide content people engage with and where, changes in experiences of self-harm or suicide behaviors associated with web-based self-harm or suicide content engagement, the disengagement-reengagement cycle, and future perspectives on web-based self-harm or suicide content engagement. Initial engagements were driven by participants seeking help, often when offline support had been unavailable. Some participants’ exposure to self-harm and suicide content led to their own self-harm and suicide behaviors, with varying patterns of change over time. Notably, disengagement from web-based self-harm and suicide spaces served as a protective measure for all participants, but the pull of familiar content resulted in only brief periods of disconnection. Participants also expressed future intentions to continue returning to these self-harm and suicide web-based spaces, acknowledging the nonlinear nature of their own recovery journey and aiming to support others in the community. Within the themes identified in this study, narratives revealed that participants’ behavior was shaped by cognitive flexibility and rigidity, metacognitive abilities, and digital expertise. Opportunities for behavior change arose during periods of cognitive flexibility prompted by life events, stressors, and shifts in mental health. Participants sought diverse and potentially harmful content during challenging times but moved toward recovery-oriented engagements in positive circumstances. Metacognitive and digital efficacy skills also played a pivotal role in participants’ control of web-based interactions, enabling more effective management of content or platforms or sites that posed potential harms. Conclusions: This study demonstrated the complexity of web-based interactions, with beneficial and harmful content intertwined. Participants who demonstrated metacognition and digital efficacy had better control over web-based engagements. Some attributed these skills to study processes, including taking part in reflective diaries, showing the potential of upskilling users. This study also highlighted how participants remained vulnerable by engaging with familiar web-based spaces, emphasizing the responsibility of web-based industry leaders to develop tools that empower users to enhance their web-based safety. %M 38546718 %R 10.2196/47699 %U https://infodemiology.jmir.org/2024/1/e47699 %U https://doi.org/10.2196/47699 %U http://www.ncbi.nlm.nih.gov/pubmed/38546718 %0 Journal Article %@ 2564-1891 %I JMIR Publications %V 4 %N %P e44395 %T The Role of Social Media in Knowledge, Perceptions, and Self-Reported Adherence Toward COVID-19 Prevention Guidelines: Cross-Sectional Study %A Garrett,Camryn %A Qiao,Shan %A Li,Xiaoming %+ Department of Health Promotion, Education, and Behavior, Arnold School of Public Health, The University of South Carolina, Discovery Building I, 915 Greene Street, Columbia, SC, 29208, United States, 1 803 777 6844, camrynmg@email.sc.edu %K COVID-19 %K digital media %K social media %K TikTok %K Instagram %K Twitter %K Facebook %K prevention guidelines %D 2024 %7 16.2.2024 %9 Original Paper %J JMIR Infodemiology %G English %X Background: Throughout the COVID-19 pandemic, social media has served as a channel of communication, a venue for entertainment, and a mechanism for information dissemination. Objective: This study aims to assess the associations between social media use patterns; demographics; and knowledge, perceptions, and self-reported adherence toward COVID-19 prevention guidelines, due to growing and evolving social media use. Methods: Quota-sampled data were collected through a web-based survey of US adults through the Qualtrics platform, from March 15, 2022, to March 23, 2022, to assess covariates (eg, demographics, vaccination, and political affiliation), frequency of social media use, social media sources of COVID-19 information, as well as knowledge, perceptions, and self-reported adherence toward COVID-19 prevention guidelines. Three linear regression models were used for data analysis. Results: A total of 1043 participants responded to the survey, with an average age of 45.3 years, among which 49.61% (n=515) of participants were men, 66.79% (n=696) were White, 11.61% (n=121) were Black or African American, 13.15% (n=137) were Hispanic or Latino, 37.71% (n=382) were Democrat, 30.21% (n=306) were Republican, and 25% (n=260) were not vaccinated. After controlling for covariates, users of TikTok (β=–.29, 95% CI –0.58 to –0.004; P=.047) were associated with lower knowledge of COVID-19 guidelines, users of Instagram (β=–.40, 95% CI –0.68 to –0.12; P=.005) and Twitter (β=–.33, 95% CI –0.58 to –0.08; P=.01) were associated with perceiving guidelines as strict, and users of Facebook (β=–.23, 95% CI –0.42 to –0.043; P=.02) and TikTok (β=–.25, 95% CI –0.5 to -0.009; P=.04) were associated with lower adherence to the guidelines (R2 0.06-0.23). Conclusions: These results allude to the complex interactions between online and physical environments. Future interventions should be tailored to subpopulations based on their demographics and social media site use. Efforts to mitigate misinformation and implement digital public health policy must account for the impact of the digital landscape on knowledge, perceptions, and level of adherence toward prevention guidelines for effective pandemic control. %M 38194493 %R 10.2196/44395 %U https://infodemiology.jmir.org/2024/1/e44395 %U https://doi.org/10.2196/44395 %U http://www.ncbi.nlm.nih.gov/pubmed/38194493 %0 Journal Article %@ 2564-1891 %I JMIR Publications %V 4 %N %P e49756 %T Dynamic Associations Between Centers for Disease Control and Prevention Social Media Contents and Epidemic Measures During COVID-19: Infoveillance Study %A Yin,Shuhua %A Chen,Shi %A Ge,Yaorong %+ University of North Carolina at Charlotte, 9201 University City Blvd, Charlotte, NC, 28223, United States, 1 8148800738, schen56@charlotte.edu %K infoveillance %K social media %K COVID-19 %K US Centers for Disease Control and Prevention %K CDC %K topic modeling %K multivariate time series analysis %D 2024 %7 23.1.2024 %9 Original Paper %J JMIR Infodemiology %G English %X Background: Health agencies have been widely adopting social media to disseminate important information, educate the public on emerging health issues, and understand public opinions. The Centers for Disease Control and Prevention (CDC) widely used social media platforms during the COVID-19 pandemic to communicate with the public and mitigate the disease in the United States. It is crucial to understand the relationships between the CDC’s social media communications and the actual epidemic metrics to improve public health agencies’ communication strategies during health emergencies. Objective: This study aimed to identify key topics in tweets posted by the CDC during the pandemic, investigate the temporal dynamics between these key topics and the actual COVID-19 epidemic measures, and make recommendations for the CDC’s digital health communication strategies for future health emergencies. Methods: Two types of data were collected: (1) a total of 17,524 COVID-19–related English tweets posted by the CDC between December 7, 2019, and January 15, 2022, and (2) COVID-19 epidemic measures in the United States from the public GitHub repository of Johns Hopkins University from January 2020 to July 2022. Latent Dirichlet allocation topic modeling was applied to identify key topics from all COVID-19–related tweets posted by the CDC, and the final topics were determined by domain experts. Various multivariate time series analysis techniques were applied between each of the identified key topics and actual COVID-19 epidemic measures to quantify the dynamic associations between these 2 types of time series data. Results: Four major topics from the CDC’s COVID-19 tweets were identified: (1) information on the prevention of health outcomes of COVID-19; (2) pediatric intervention and family safety; (3) updates of the epidemic situation of COVID-19; and (4) research and community engagement to curb COVID-19. Multivariate analyses showed that there were significant variabilities of progression between the CDC’s topics and the actual COVID-19 epidemic measures. Some CDC topics showed substantial associations with the COVID-19 measures over different time spans throughout the pandemic, expressing similar temporal dynamics between these 2 types of time series data. Conclusions: Our study is the first to comprehensively investigate the dynamic associations between topics discussed by the CDC on Twitter and the COVID-19 epidemic measures in the United States. We identified 4 major topic themes via topic modeling and explored how each of these topics was associated with each major epidemic measure by performing various multivariate time series analyses. We recommend that it is critical for public health agencies, such as the CDC, to update and disseminate timely and accurate information to the public and align major topics with key epidemic measures over time. We suggest that social media can help public health agencies to inform the public on health emergencies and to mitigate them effectively. %M 38261367 %R 10.2196/49756 %U https://infodemiology.jmir.org/2024/1/e49756 %U https://doi.org/10.2196/49756 %U http://www.ncbi.nlm.nih.gov/pubmed/38261367 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 7 %N %P e52995 %T Automated Credibility Assessment of Web-Based Health Information Considering Health on the Net Foundation Code of Conduct (HONcode): Model Development and Validation Study %A Bayani,Azadeh %A Ayotte,Alexandre %A Nikiema,Jean Noel %+ Centre de recherche en santé publique, Université de Montréal et Centre intégré universitaire de santé et de services sociaux du Centre-Sud-de-l’Île-de-Montréal, Montréal, QC, H3C 3J7, Canada, 1 4389980241, azadeh.bayani@umontreal.ca %K HONcode %K infodemic %K natural language processing %K web-based health information %K machine learning %D 2023 %7 22.12.2023 %9 Original Paper %J JMIR Form Res %G English %X Background: An increasing number of users are turning to web-based sources as an important source of health care guidance information. Thus, trustworthy sources of information should be automatically identifiable using objective criteria. Objective: The purpose of this study was to automate the assessment of the Health on the Net Foundation Code of Conduct (HONcode) criteria, enhancing our ability to pinpoint trustworthy health information sources. Methods: A data set of 538 web pages displaying health content was collected from 43 health-related websites. HONcode criteria have been considered as web page and website levels. For the website-level criteria (confidentiality, transparency, financial disclosure, and advertising policy), a bag of keywords has been identified to assess the criteria using a rule-based model. For the web page–level criteria (authority, complementarity, justifiability, and attribution) several machine learning (ML) approaches were used. In total, 200 web pages were manually annotated until achieving a balanced representation in terms of frequency. In total, 3 ML models—random forest, support vector machines (SVM), and Bidirectional Encoder Representations from Transformers (BERT)—were trained on the initial annotated data. A second step of training was implemented for the complementarity criterion using the BERT model for multiclass classification of the complementarity sentences obtained by annotation and data augmentation (positive, negative, and noncommittal sentences). Finally, the remaining web pages were classified using the selected model and 100 sentences were randomly selected for manual review. Results: For web page–level criteria, the random forest model showed a good performance for the attribution criterion while displaying subpar performance in the others. BERT and SVM had a stable performance across all the criteria. BERT had a better area under the curve (AUC) of 0.96, 0.98, and 1.00 for neutral sentences, justifiability, and attribution, respectively. SVM had the overall better performance for the classification of complementarity with the AUC equal to 0.98. Finally, SVM and BERT had an equal AUC of 0.98 for the authority criterion. For the website level criteria, the rule-based model was able to retrieve web pages with an accuracy of 0.97 for confidentiality, 0.82 for transparency, and 0.51 for both financial disclosure and advertising policy. The final evaluation of the sentences determined 0.88 of precision and the agreement level of reviewers was computed at 0.82. Conclusions: Our results showed the potential power of automating the HONcode criteria assessment using ML approaches. This approach could be used with different types of pretrained models to accelerate the text annotation, and classification and to improve the performance in low-resource cases. Further work needs to be conducted to determine how to assign different weights to the criteria, as well as to identify additional characteristics that should be considered for consolidating these criteria into a comprehensive reliability score. %M 38133919 %R 10.2196/52995 %U https://formative.jmir.org/2023/1/e52995 %U https://doi.org/10.2196/52995 %U http://www.ncbi.nlm.nih.gov/pubmed/38133919 %0 Journal Article %@ 2564-1891 %I JMIR Publications %V 3 %N %P e51760 %T Effective Infodemic Management: A Substantive Article of the Pandemic Accord %A Taguchi,Kazuho %A Matsoso,Precious %A Driece,Roland %A da Silva Nunes,Tovar %A Soliman,Ahmed %A Tangcharoensathien,Viroj %+ International Health Policy Program, Ministry of Public Health, Tivanond Road, Nonthaburi, 11000, Thailand, 66 818480297, viroj@ihpp.thaigov.net %K Pandemic Accord %K infodemic %K infodemic management %K COVID-19 %K social media %K Intergovernmental Negotiating Body %K INB %K INB Bureau %K World Health Organization %K WHO %K misinformation %K disinformation %K public health %D 2023 %7 20.9.2023 %9 Editorial %J JMIR Infodemiology %G English %X Social media has proven to be valuable for disseminating public health information during pandemics. However, the circulation of misinformation through social media during public health emergencies, such as the SARS (severe acute respiratory syndrome), Ebola, and COVID-19 pandemics, has seriously hampered effective responses, leading to negative consequences. Intentionally misleading and deceptive fake news aims to harm organizations and individuals. To effectively respond to misinformation, governments should strengthen the management of an “infodemic,” which involves monitoring the impact of infodemics through social listening, detecting signals of infodemic spread, mitigating the harmful effects of infodemics, and strengthening the resilience of individuals and communities. The global spread of misinformation requires multisectoral collaboration, such as researchers identifying leading sources of misinformation and superspreaders, media agencies identifying and debunking misinformation, technology platforms reducing the distribution of false or misleading posts and guiding users to health information from credible sources, and governments disseminating clear public health information in partnership with trusted messengers. Additionally, fact-checking has room for improvement through the use of automated checks. Collaboration between governments and fact-checking agencies should also be strengthened via effective and timely debunking mechanisms. Though the Intergovernmental Negotiating Body (INB) has yet to define the term “infodemic,” Article 18 of the INB Bureau’s text, developed for the Pandemic Accord, encompasses a range of actions aimed at enhancing infodemic management. The INB Bureau continues to facilitate evidence-informed discussion for an implementable article on infodemic management. %M 37728969 %R 10.2196/51760 %U https://infodemiology.jmir.org/2023/1/e51760 %U https://doi.org/10.2196/51760 %U http://www.ncbi.nlm.nih.gov/pubmed/37728969 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 25 %N %P e49061 %T Exploring YouTube’s Recommendation System in the Context of COVID-19 Vaccines: Computational and Comparative Analysis of Video Trajectories %A Ng,Yee Man Margaret %A Hoffmann Pham,Katherine %A Luengo-Oroz,Miguel %+ Department of Journalism & Institute of Communications Research, University of Illinois at Urbana-Champaign, 810 S Wright St, Champaign, IL, 61801, United States, 1 217 300 8186, margaretnym@gmail.com %K algorithmic auditing %K antivaccine sentiment %K crowdsourcing %K recommendation systems %K watch history %K YouTube %D 2023 %7 15.9.2023 %9 Original Paper %J J Med Internet Res %G English %X Background: Throughout the COVID-19 pandemic, there has been a concern that social media may contribute to vaccine hesitancy due to the wide availability of antivaccine content on social media platforms. YouTube has stated its commitment to removing content that contains misinformation on vaccination. Nevertheless, such claims are difficult to audit. There is a need for more empirical research to evaluate the actual prevalence of antivaccine sentiment on the internet. Objective: This study examines recommendations made by YouTube’s algorithms in order to investigate whether the platform may facilitate the spread of antivaccine sentiment on the internet. We assess the prevalence of antivaccine sentiment in recommended videos and evaluate how real-world users’ experiences are different from the personalized recommendations obtained by using synthetic data collection methods, which are often used to study YouTube’s recommendation systems. Methods: We trace trajectories from a credible seed video posted by the World Health Organization to antivaccine videos, following only video links suggested by YouTube’s recommendation system. First, we gamify the process by asking real-world participants to intentionally find an antivaccine video with as few clicks as possible. Having collected crowdsourced trajectory data from respondents from (1) the World Health Organization and United Nations system (nWHO/UN=33) and (2) Amazon Mechanical Turk (nAMT=80), we next compare the recommendations seen by these users to recommended videos that are obtained from (3) the YouTube application programming interface’s RelatedToVideoID parameter (nRTV=40) and (4) from clean browsers without any identifying cookies (nCB=40), which serve as reference points. We develop machine learning methods to classify antivaccine content at scale, enabling us to automatically evaluate 27,074 video recommendations made by YouTube. Results: We found no evidence that YouTube promotes antivaccine content; the average share of antivaccine videos remained well below 6% at all steps in users’ recommendation trajectories. However, the watch histories of users significantly affect video recommendations, suggesting that data from the application programming interface or from a clean browser do not offer an accurate picture of the recommendations that real users are seeing. Real users saw slightly more provaccine content as they advanced through their recommendation trajectories, whereas synthetic users were drawn toward irrelevant recommendations as they advanced. Rather than antivaccine content, videos recommended by YouTube are likely to contain health-related content that is not specifically related to vaccination. These videos are usually longer and contain more popular content. Conclusions: Our findings suggest that the common perception that YouTube’s recommendation system acts as a “rabbit hole” may be inaccurate and that YouTube may instead be following a “blockbuster” strategy that attempts to engage users by promoting other content that has been reliably successful across the platform. %M 37713243 %R 10.2196/49061 %U https://www.jmir.org/2023/1/e49061 %U https://doi.org/10.2196/49061 %U http://www.ncbi.nlm.nih.gov/pubmed/37713243 %0 Journal Article %@ 2564-1891 %I JMIR Publications %V 3 %N %P e47317 %T Using Machine Learning Technology (Early Artificial Intelligence–Supported Response With Social Listening Platform) to Enhance Digital Social Understanding for the COVID-19 Infodemic: Development and Implementation Study %A White,Becky K %A Gombert,Arnault %A Nguyen,Tim %A Yau,Brian %A Ishizumi,Atsuyoshi %A Kirchner,Laura %A León,Alicia %A Wilson,Harry %A Jaramillo-Gutierrez,Giovanna %A Cerquides,Jesus %A D’Agostino,Marcelo %A Salvi,Cristiana %A Sreenath,Ravi Shankar %A Rambaud,Kimberly %A Samhouri,Dalia %A Briand,Sylvie %A Purnat,Tina D %+ Department of Epidemic and Pandemic Preparedness and Prevention, World Health Organization, Ave Appia 21, Geneva, 1202, Switzerland, 41 227912111, purnatt@who.int %K infodemic %K sentiment %K narrative analysis %K social listening %K natural language processing %K social media %K public health %K pandemic preparedness %K pandemic response %K artificial intelligence %K AI text analytics %K COVID-19 %K information voids %K machine learning %D 2023 %7 21.8.2023 %9 Original Paper %J JMIR Infodemiology %G English %X Background: Amid the COVID-19 pandemic, there has been a need for rapid social understanding to inform infodemic management and response. Although social media analysis platforms have traditionally been designed for commercial brands for marketing and sales purposes, they have been underused and adapted for a comprehensive understanding of social dynamics in areas such as public health. Traditional systems have challenges for public health use, and new tools and innovative methods are required. The World Health Organization Early Artificial Intelligence–Supported Response with Social Listening (EARS) platform was developed to overcome some of these challenges. Objective: This paper describes the development of the EARS platform, including data sourcing, development, and validation of a machine learning categorization approach, as well as the results from the pilot study. Methods: Data for EARS are collected daily from web-based conversations in publicly available sources in 9 languages. Public health and social media experts developed a taxonomy to categorize COVID-19 narratives into 5 relevant main categories and 41 subcategories. We developed a semisupervised machine learning algorithm to categorize social media posts into categories and various filters. To validate the results obtained by the machine learning–based approach, we compared it to a search-filter approach, applying Boolean queries with the same amount of information and measured the recall and precision. Hotelling T2 was used to determine the effect of the classification method on the combined variables. Results: The EARS platform was developed, validated, and applied to characterize conversations regarding COVID-19 since December 2020. A total of 215,469,045 social posts were collected for processing from December 2020 to February 2022. The machine learning algorithm outperformed the Boolean search filters method for precision and recall in both English and Spanish languages (P<.001). Demographic and other filters provided useful insights on data, and the gender split of users in the platform was largely consistent with population-level data on social media use. Conclusions: The EARS platform was developed to address the changing needs of public health analysts during the COVID-19 pandemic. The application of public health taxonomy and artificial intelligence technology to a user-friendly social listening platform, accessible directly by analysts, is a significant step in better enabling understanding of global narratives. The platform was designed for scalability; iterations and new countries and languages have been added. This research has shown that a machine learning approach is more accurate than using only keywords and has the benefit of categorizing and understanding large amounts of digital social data during an infodemic. Further technical developments are needed and planned for continuous improvements, to meet the challenges in the generation of infodemic insights from social media for infodemic managers and public health professionals. %M 37422854 %R 10.2196/47317 %U https://infodemiology.jmir.org/2023/1/e47317 %U https://doi.org/10.2196/47317 %U http://www.ncbi.nlm.nih.gov/pubmed/37422854 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 25 %N %P e43841 %T Misinformation and Public Health Messaging in the Early Stages of the Mpox Outbreak: Mapping the Twitter Narrative With Deep Learning %A Edinger,Andy %A Valdez,Danny %A Walsh-Buhi,Eric %A Trueblood,Jennifer S %A Lorenzo-Luaces,Lorenzo %A Rutter,Lauren A %A Bollen,Johan %+ Center for Social and Biomedical Complexity, Indiana University, 700 N Woodlawn Ave, Bloomington, IN, 47408, United States, 1 14192799439, aedinger7@gmail.com %K COVID-19 %K deep learning %K misinformation %K monkeypox %K mpox %K outbreak %K public health %K social media %K Twitter %D 2023 %7 6.6.2023 %9 Original Paper %J J Med Internet Res %G English %X Background: Shortly after the worst of the COVID-19 pandemic, an outbreak of mpox introduced another critical public health emergency. Like the COVID-19 pandemic, the mpox outbreak was characterized by a rising prevalence of public health misinformation on social media, through which many US adults receive and engage with news. Digital misinformation continues to challenge the efforts of public health officials in providing accurate and timely information to the public. We examine the evolving topic distributions of social media narratives during the mpox outbreak to map the tension between rapidly diffusing misinformation and public health communication. Objective: This study aims to observe topical themes occurring in a large-scale collection of tweets about mpox using deep learning. Methods: We leveraged a data set comprised of all mpox-related tweets that were posted between May 7, 2022, and July 23, 2022. We then applied Sentence Bidirectional Encoder Representations From Transformers (S-BERT) to the content of each tweet to generate a representation of its content in high-dimensional vector space, where semantically similar tweets will be located closely together. We projected the set of tweet embeddings to a 2D map by applying principal component analysis and Uniform Manifold Approximation Projection (UMAP). Finally, we group these data points into 7 topical clusters using k-means clustering and analyze each cluster to determine its dominant topics. We analyze the prevalence of each cluster over time to evaluate longitudinal thematic changes. Results: Our deep-learning pipeline revealed 7 distinct clusters of content: (1) cynicism, (2) exasperation, (3) COVID-19, (4) men who have sex with men, (5) case reports, (6) vaccination, and (7) World Health Organization (WHO). Clusters that largely communicated erroneous or irrelevant information began earlier and grew faster, reaching a wider audience than later communications by official instances and health officials. Conclusions: Within a few weeks of the first reported mpox cases, an avalanche of mostly false, misleading, irrelevant, or damaging information started to circulate on social media. Official institutions, including the WHO, acted promptly, providing case reports and accurate information within weeks, but were overshadowed by rapidly spreading social media chatter. Our results point to the need for real-time monitoring of social media content to optimize responses to public health emergencies. %M 37163694 %R 10.2196/43841 %U https://www.jmir.org/2023/1/e43841 %U https://doi.org/10.2196/43841 %U http://www.ncbi.nlm.nih.gov/pubmed/37163694 %0 Journal Article %@ 2564-1891 %I JMIR Publications %V 3 %N %P e43646 %T Establishing Infodemic Management in Germany: A Framework for Social Listening and Integrated Analysis to Report Infodemic Insights at the National Public Health Institute %A Boender,T Sonia %A Schneider,Paula Helene %A Houareau,Claudia %A Wehrli,Silvan %A Purnat,Tina D %A Ishizumi,Atsuyoshi %A Wilhelm,Elisabeth %A Voegeli,Christopher %A Wieler,Lothar H %A Leuker,Christina %+ Risk Communication Unit, Robert Koch Institute, Nordufer 20, Berlin, 13353, Germany, 49 030 187540, p1@rki.de %K infodemic %K social listening %K communication %K infodemiology %K public health %K health promotion %K misinformation %K integrated analysis %K infodemic insights %D 2023 %7 1.6.2023 %9 Original Paper %J JMIR Infodemiology %G English %X Background: To respond to the need to establish infodemic management functions at the national public health institute in Germany (Robert Koch Institute, RKI), we explored and assessed available data sources, developed a social listening and integrated analysis framework, and defined when infodemic management functions should be activated during emergencies. Objective: We aimed to establish a framework for social listening and integrated analysis for public health in the German context using international examples and technical guidance documents for infodemic management. Methods: This study completed the following objectives: identified (potentially) available data sources for social listening and integrated analysis; assessed these data sources for their suitability and usefulness for integrated analysis in addition to an assessment of their risk using the RKI’s standardized data protection requirements; developed a framework and workflow to combine social listening and integrated analysis to report back actionable infodemic insights for public health communications by the RKI and stakeholders; and defined criteria for activating integrated analysis structures in the context of a specific health event or health emergency. Results: We included and classified 38% (16/42) of the identified and assessed data sources for social listening and integrated analysis at the RKI into 3 categories: social media and web-based listening data, RKI-specific data, and infodemic insights. Most data sources can be analyzed weekly to detect current trends and narratives and to inform a timely response by reporting insights that include a risk assessment and scalar judgments of different narratives and themes. Conclusions: This study identified, assessed, and prioritized a wide range of data sources for social listening and integrated analysis to report actionable infodemic insights, ensuring a valuable first step in establishing and operationalizing infodemic management at the RKI. This case study also serves as a roadmap for others. Ultimately, once operational, these activities will inform better and targeted public health communication at the RKI and beyond. %M 37261891 %R 10.2196/43646 %U https://infodemiology.jmir.org/2023/1/e43646 %U https://doi.org/10.2196/43646 %U http://www.ncbi.nlm.nih.gov/pubmed/37261891 %0 Journal Article %@ 2564-1891 %I JMIR Publications %V 3 %N %P e44714 %T Global Misinformation Spillovers in the Vaccination Debate Before and During the COVID-19 Pandemic: Multilingual Twitter Study %A Lenti,Jacopo %A Mejova,Yelena %A Kalimeri,Kyriaki %A Panisson,André %A Paolotti,Daniela %A Tizzani,Michele %A Starnini,Michele %+ Departament de Fisica, Universitat Politecnica de Catalunya, Campus Nord B4, Barcelona, 08034, Spain, 34 934 01 62 00, michele.starnini@upc.edu %K vaccination hesitancy %K vaccine %K misinformation %K Twitter %K social media %K COVID-19 %D 2023 %7 24.5.2023 %9 Original Paper %J JMIR Infodemiology %G English %X Background: Antivaccination views pervade online social media, fueling distrust in scientific expertise and increasing the number of vaccine-hesitant individuals. Although previous studies focused on specific countries, the COVID-19 pandemic has brought the vaccination discourse worldwide, underpinning the need to tackle low-credible information flows on a global scale to design effective countermeasures. Objective: This study aimed to quantify cross-border misinformation flows among users exposed to antivaccination (no-vax) content and the effects of content moderation on vaccine-related misinformation. Methods: We collected 316 million vaccine-related Twitter (Twitter, Inc) messages in 18 languages from October 2019 to March 2021. We geolocated users in 28 different countries and reconstructed a retweet network and cosharing network for each country. We identified communities of users exposed to no-vax content by detecting communities in the retweet network via hierarchical clustering and manual annotation. We collected a list of low-credibility domains and quantified the interactions and misinformation flows among no-vax communities of different countries. Results: The findings showed that during the pandemic, no-vax communities became more central in the country-specific debates and their cross-border connections strengthened, revealing a global Twitter antivaccination network. US users are central in this network, whereas Russian users also became net exporters of misinformation during vaccination rollout. Interestingly, we found that Twitter’s content moderation efforts, in particular the suspension of users following the January 6 US Capitol attack, had a worldwide impact in reducing the spread of misinformation about vaccines. Conclusions: These findings may help public health institutions and social media platforms mitigate the spread of health-related, low-credibility information by revealing vulnerable web-based communities. %M 37223965 %R 10.2196/44714 %U https://infodemiology.jmir.org/2023/1/e44714 %U https://doi.org/10.2196/44714 %U http://www.ncbi.nlm.nih.gov/pubmed/37223965 %0 Journal Article %@ 2564-1891 %I JMIR Publications %V 3 %N %P e40913 %T Potential Impact of the COVID-19 Pandemic on Public Perception of Water Pipes on Reddit: Observational Study %A Zheng,Zihe %A Xie,Zidian %A Goniewicz,Maciej %A Rahman,Irfan %A Li,Dongmei %+ Department of Clinical and Translational Research, University of Rochester Medical Center, 265 Crittenden Boulevard Cu 420708, Rochester, NY, 14642-0001, United States, 1 5852767285, Dongmei_Li@urmc.rochester.edu %K water pipes %K Reddit %K COVID-19 %K COVID-19 pandemic %K public perception %D 2023 %7 20.4.2023 %9 Original Paper %J JMIR Infodemiology %G English %X Background: Socializing is one of the main motivations for water pipe smoking. Restrictions on social gatherings during the COVID-19 pandemic might have influenced water pipe smokers’ behaviors. As one of the most popular social media platforms, Reddit has been used to study public opinions and user experiences. Objective: In this study, we aimed to examine the influence of the COVID-19 pandemic on public perception and discussion of water pipe tobacco smoking using Reddit data. Methods: We collected Reddit posts between December 1, 2018, and June 30, 2021, from a Reddit archive (PushShift) using keywords such as “waterpipe,” “hookah,” and “shisha.” We examined the temporal trend in Reddit posts mentioning water pipes and different locations (such as homes and lounges or bars). The temporal trend was further tested using interrupted time series analysis. Sentiment analysis was performed to study the change in sentiment of water pipe–related posts before and during the pandemic. Topic modeling using latent Dirichlet allocation (LDA) was used to examine major topics discussed in water pipe–related posts before and during the pandemic. Results: A total of 45,765 nonpromotion water pipe–related Reddit posts were collected and used for data analysis. We found that the weekly number of Reddit posts mentioning water pipes significantly increased at the beginning of the COVID-19 pandemic (P<.001), and gradually decreased afterward (P<.001). In contrast, Reddit posts mentioning water pipes and lounges or bars showed an opposite trend. Compared to the period before the COVID-19 pandemic, the average number of Reddit posts mentioning lounges or bars was lower at the beginning of the pandemic but gradually increased afterward, while the average number of Reddit posts mentioning the word “home” remained similar during the COVID-19 pandemic (P=.29). While water pipe–related posts with a positive sentiment were dominant (12,526/21,182, 59.14% before the pandemic; 14,686/24,583, 59.74% after the pandemic), there was no change in the proportion of water pipe–related posts with different sentiments before and during the pandemic (P=.19, P=.26, and P=.65 for positive, negative, and neutral posts, respectively). Most topics related to water pipes on Reddit were similar before and during the pandemic. There were more discussions about the opening and closing of hookah lounges or bars during the pandemic. Conclusions: This study provides a first evaluation of the possible impact of the COVID-19 pandemic on public perceptions of and discussions about water pipes on Reddit. %M 37124245 %R 10.2196/40913 %U https://infodemiology.jmir.org/2023/1/e40913 %U https://doi.org/10.2196/40913 %U http://www.ncbi.nlm.nih.gov/pubmed/37124245 %0 Journal Article %@ 2564-1891 %I JMIR Publications %V 3 %N %P e43694 %T The Early Detection of Fraudulent COVID-19 Products From Twitter Chatter: Data Set and Baseline Approach Using Anomaly Detection %A Sarker,Abeed %A Lakamana,Sahithi %A Liao,Ruqi %A Abbas,Aamir %A Yang,Yuan-Chi %A Al-Garadi,Mohammed %+ Department of Biomedical Informatics, School of Medicine, Emory University, 101 Woodruff Circle, Suite 4101, Atlanta, GA, 30030, United States, 1 6024746203, abeed@dbmi.emory.edu %K coronavirus %K COVID-19 drug treatment %K social media %K infodemiology %K public health surveillance %K COVID-19 %K misinformation %K natural language processing %K neural network %K data mining %D 2023 %7 14.3.2023 %9 Original Paper %J JMIR Infodemiology %G English %X Background: Social media has served as a lucrative platform for spreading misinformation and for promoting fraudulent products for the treatment, testing, and prevention of COVID-19. This has resulted in the issuance of many warning letters by the US Food and Drug Administration (FDA). While social media continues to serve as the primary platform for the promotion of such fraudulent products, it also presents the opportunity to identify these products early by using effective social media mining methods. Objective: Our objectives were to (1) create a data set of fraudulent COVID-19 products that can be used for future research and (2) propose a method using data from Twitter for automatically detecting heavily promoted COVID-19 products early. Methods: We created a data set from FDA-issued warnings during the early months of the COVID-19 pandemic. We used natural language processing and time-series anomaly detection methods for automatically detecting fraudulent COVID-19 products early from Twitter. Our approach is based on the intuition that increases in the popularity of fraudulent products lead to corresponding anomalous increases in the volume of chatter regarding them. We compared the anomaly signal generation date for each product with the corresponding FDA letter issuance date. We also performed a brief manual analysis of chatter associated with 2 products to characterize their contents. Results: FDA warning issue dates ranged from March 6, 2020, to June 22, 2021, and 44 key phrases representing fraudulent products were included. From 577,872,350 posts made between February 19 and December 31, 2020, which are all publicly available, our unsupervised approach detected 34 out of 44 (77.3%) signals about fraudulent products earlier than the FDA letter issuance dates, and an additional 6 (13.6%) within a week following the corresponding FDA letters. Content analysis revealed misinformation, information, political, and conspiracy theories to be prominent topics. Conclusions: Our proposed method is simple, effective, easy to deploy, and does not require high-performance computing machinery unlike deep neural network–based methods. The method can be easily extended to other types of signal detection from social media data. The data set may be used for future research and the development of more advanced methods. %M 37113382 %R 10.2196/43694 %U https://infodemiology.jmir.org/2023/1/e43694 %U https://doi.org/10.2196/43694 %U http://www.ncbi.nlm.nih.gov/pubmed/37113382 %0 Journal Article %@ 2564-1891 %I JMIR Publications %V 3 %N %P e44207 %T Measuring the Burden of Infodemics: Summary of the Methods and Results of the Fifth WHO Infodemic Management Conference %A Wilhelm,Elisabeth %A Ballalai,Isabella %A Belanger,Marie-Eve %A Benjamin,Peter %A Bertrand-Ferrandis,Catherine %A Bezbaruah,Supriya %A Briand,Sylvie %A Brooks,Ian %A Bruns,Richard %A Bucci,Lucie M %A Calleja,Neville %A Chiou,Howard %A Devaria,Abhinav %A Dini,Lorena %A D'Souza,Hyjel %A Dunn,Adam G %A Eichstaedt,Johannes C %A Evers,Silvia M A A %A Gobat,Nina %A Gissler,Mika %A Gonzales,Ian Christian %A Gruzd,Anatoliy %A Hess,Sarah %A Ishizumi,Atsuyoshi %A John,Oommen %A Joshi,Ashish %A Kaluza,Benjamin %A Khamis,Nagwa %A Kosinska,Monika %A Kulkarni,Shibani %A Lingri,Dimitra %A Ludolph,Ramona %A Mackey,Tim %A Mandić-Rajčević,Stefan %A Menczer,Filippo %A Mudaliar,Vijaybabu %A Murthy,Shruti %A Nazakat,Syed %A Nguyen,Tim %A Nilsen,Jennifer %A Pallari,Elena %A Pasternak Taschner,Natalia %A Petelos,Elena %A Prinstein,Mitchell J %A Roozenbeek,Jon %A Schneider,Anton %A Srinivasan,Varadharajan %A Stevanović,Aleksandar %A Strahwald,Brigitte %A Syed Abdul,Shabbir %A Varaidzo Machiri,Sandra %A van der Linden,Sander %A Voegeli,Christopher %A Wardle,Claire %A Wegwarth,Odette %A White,Becky K %A Willie,Estelle %A Yau,Brian %A Purnat,Tina D %+ Department of Epidemic and Pandemic Preparedness and Prevention, World Health Organization, Avenue Appia 20, Geneva, 1211, Switzerland, 41 22 791 21 11, purnatt@who.int %K COVID-19 %K infodemic %K burden of infodemic %K infodemic management %K infodemic metrics %K World Health Organization %K technical consultation %K infodemiology %D 2023 %7 20.2.2023 %9 Original Paper %J JMIR Infodemiology %G English %X Background: An infodemic is excess information, including false or misleading information, that spreads in digital and physical environments during a public health emergency. The COVID-19 pandemic has been accompanied by an unprecedented global infodemic that has led to confusion about the benefits of medical and public health interventions, with substantial impact on risk-taking and health-seeking behaviors, eroding trust in health authorities and compromising the effectiveness of public health responses and policies. Standardized measures are needed to quantify the harmful impacts of the infodemic in a systematic and methodologically robust manner, as well as harmonizing highly divergent approaches currently explored for this purpose. This can serve as a foundation for a systematic, evidence-based approach to monitoring, identifying, and mitigating future infodemic harms in emergency preparedness and prevention. Objective: In this paper, we summarize the Fifth World Health Organization (WHO) Infodemic Management Conference structure, proceedings, outcomes, and proposed actions seeking to identify the interdisciplinary approaches and frameworks needed to enable the measurement of the burden of infodemics. Methods: An iterative human-centered design (HCD) approach and concept mapping were used to facilitate focused discussions and allow for the generation of actionable outcomes and recommendations. The discussions included 86 participants representing diverse scientific disciplines and health authorities from 28 countries across all WHO regions, along with observers from civil society and global public health–implementing partners. A thematic map capturing the concepts matching the key contributing factors to the public health burden of infodemics was used throughout the conference to frame and contextualize discussions. Five key areas for immediate action were identified. Results: The 5 key areas for the development of metrics to assess the burden of infodemics and associated interventions included (1) developing standardized definitions and ensuring the adoption thereof; (2) improving the map of concepts influencing the burden of infodemics; (3) conducting a review of evidence, tools, and data sources; (4) setting up a technical working group; and (5) addressing immediate priorities for postpandemic recovery and resilience building. The summary report consolidated group input toward a common vocabulary with standardized terms, concepts, study designs, measures, and tools to estimate the burden of infodemics and the effectiveness of infodemic management interventions. Conclusions: Standardizing measurement is the basis for documenting the burden of infodemics on health systems and population health during emergencies. Investment is needed into the development of practical, affordable, evidence-based, and systematic methods that are legally and ethically balanced for monitoring infodemics; generating diagnostics, infodemic insights, and recommendations; and developing interventions, action-oriented guidance, policies, support options, mechanisms, and tools for infodemic managers and emergency program managers. %M 37012998 %R 10.2196/44207 %U https://infodemiology.jmir.org/2023/1/e44207 %U https://doi.org/10.2196/44207 %U http://www.ncbi.nlm.nih.gov/pubmed/37012998 %0 Journal Article %@ 2564-1891 %I JMIR Publications %V 3 %N %P e38607 %T COVID-19–Associated Misinformation Across the South Asian Diaspora: Qualitative Study of WhatsApp Messages %A Sharma,Anjana E %A Khosla,Kiran %A Potharaju,Kameswari %A Mukherjea,Arnab %A Sarkar,Urmimala %+ Department of Family and Community Medicine, University of California San Francisco, 1001 Potrero Avenue, Zuckerberg San Francisco General Hospital, San Francisco, CA, 94143, United States, 1 628 206 4943, anjana.sharma@ucsf.edu %K misinformation %K COVID-19 %K South Asians %K disparities %K social media %K infodemiology %K WhatsApp %K messages %K apps %K health information %K reliability %K communication %K Asian %K English %K community %K health %K organization %K public health %K pandemic %D 2023 %7 5.1.2023 %9 Original Paper %J JMIR Infodemiology %G English %X Background: South Asians, inclusive of individuals originating in India, Pakistan, Maldives, Bangladesh, Sri Lanka, Bhutan, and Nepal, comprise the largest diaspora in the world, with large South Asian communities residing in the Caribbean, Africa, Europe, and elsewhere. There is evidence that South Asian communities have disproportionately experienced COVID-19 infections and mortality. WhatsApp, a free messaging app, is widely used in transnational communication within the South Asian diaspora. Limited studies exist on COVID-19–related misinformation specific to the South Asian community on WhatsApp. Understanding communication on WhatsApp may improve public health messaging to address COVID-19 disparities among South Asian communities worldwide. Objective: We developed the COVID-19–Associated misinfoRmation On Messaging apps (CAROM) study to identify messages containing misinformation about COVID-19 shared via WhatsApp. Methods: We collected messages forwarded globally through WhatsApp from self-identified South Asian community members between March 23 and June 3, 2021. We excluded messages that were in languages other than English, did not contain misinformation, or were not relevant to COVID-19. We deidentified each message and coded them for one or more content categories, media types (eg, video, image, text, web link, or a combination of these elements), and tone (eg, fearful, well intentioned, or pleading). We then performed a qualitative content analysis to arrive at key themes of COVID-19 misinformation. Results: We received 108 messages; 55 messages met the inclusion criteria for the final analytic sample; 32 (58%) contained text, 15 (27%) contained images, and 13 (24%) contained video. Content analysis revealed the following themes: “community transmission” relating to misinformation on how COVID-19 spreads in the community; “prevention” and “treatment,” including Ayurvedic and traditional remedies for how to prevent or treat COVID-19 infection; and messaging attempting to sell “products or services” to prevent or cure COVID-19. Messages varied in audience from the general public to South Asians specifically; the latter included messages alluding to South Asian pride and solidarity. Scientific jargon and references to major organizations and leaders in health care were included to provide credibility. Messages with a pleading tone encouraged users to forward them to friends or family. Conclusions: Misinformation in the South Asian community on WhatsApp spreads erroneous ideas regarding disease transmission, prevention, and treatment. Content evoking solidarity, “trustworthy” sources, and encouragement to forward messages may increase the spread of misinformation. Public health outlets and social media companies must actively combat misinformation to address health disparities among the South Asian diaspora during the COVID-19 pandemic and in future public health emergencies. %M 37113380 %R 10.2196/38607 %U https://infodemiology.jmir.org/2023/1/e38607 %U https://doi.org/10.2196/38607 %U http://www.ncbi.nlm.nih.gov/pubmed/37113380 %0 Journal Article %@ 2564-1891 %I JMIR Publications %V 2 %N 2 %P e38441 %T COVID-19 Messaging on Social Media for American Indian and Alaska Native Communities: Thematic Analysis of Audience Reach and Web Behavior %A Weeks,Rose %A White,Sydney %A Hartner,Anna-Maria %A Littlepage,Shea %A Wolf,Jennifer %A Masten,Kristin %A Tingey,Lauren %+ Center for Indigenous Health, Department of International Health, Johns Hopkins Bloomberg School of Public Health, 415 N Washington Street, 5th Floor, Baltimore, MD, 21231, United States, 1 443 287 4832, rweeks@jhu.edu %K COVID-19 %K American Indian or Alaska Native %K social media %K communication %K tribal organization %K community health %K infodemiology %K Twitter %K online behavior %K content analysis %K thematic analysis %D 2022 %7 25.11.2022 %9 Original Paper %J JMIR Infodemiology %G English %X Background: During the COVID-19 pandemic, tribal and health organizations used social media to rapidly disseminate public health guidance highlighting protective behaviors such as masking and vaccination to mitigate the pandemic’s disproportionate burden on American Indian and Alaska Native (AI/AN) communities. Objective: Seeking to provide guidance for future communication campaigns prioritizing AI/AN audiences, this study aimed to identify Twitter post characteristics associated with higher performance, measured by audience reach (impressions) and web behavior (engagement rate). Methods: We analyzed Twitter posts published by a campaign by the Johns Hopkins Center for Indigenous Health from July 2020 to June 2021. Qualitative analysis was informed by in-depth interviews with members of a Tribal Advisory Board and thematically organized according to the Health Belief Model. A general linearized model was used to analyze associations between Twitter post themes, impressions, and engagement rates. Results: The campaign published 162 Twitter messages, which organically generated 425,834 impressions and 6016 engagements. Iterative analysis of these Twitter posts identified 10 unique themes under theory- and culture-related categories of framing knowledge, cultural messaging, normalizing mitigation strategies, and interactive opportunities, which were corroborated by interviews with Tribal Advisory Board members. Statistical analysis of Twitter impressions and engagement rate by theme demonstrated that posts featuring culturally resonant community role models (P=.02), promoting web-based events (P=.002), and with messaging as part of Twitter Chats (P<.001) were likely to generate higher impressions. In the adjusted analysis controlling for the date of posting, only the promotion of web-based events (P=.003) and Twitter Chat messaging (P=.01) remained significant. Visual, explanatory posts promoting self-efficacy (P=.01; P=.01) and humorous posts (P=.02; P=.01) were the most likely to generate high–engagement rates in both the adjusted and unadjusted analysis. Conclusions: Results from the 1-year Twitter campaign provide lessons to inform organizations designing social media messages to reach and engage AI/AN social media audiences. The use of interactive events, instructional graphics, and Indigenous humor are promising practices to engage community members, potentially opening audiences to receiving important and time-sensitive guidance. %M 36471705 %R 10.2196/38441 %U https://infodemiology.jmir.org/2022/2/e38441 %U https://doi.org/10.2196/38441 %U http://www.ncbi.nlm.nih.gov/pubmed/36471705 %0 Journal Article %@ 2564-1891 %I JMIR Publications %V 2 %N 2 %P e39504 %T Monitoring Mentions of COVID-19 Vaccine Side Effects on Japanese and Indonesian Twitter: Infodemiological Study %A Ferawati,Kiki %A Liew,Kongmeng %A Aramaki,Eiji %A Wakamiya,Shoko %+ Graduate School of Science and Technology, Nara Institute of Science and Technology, 8916-5, Takayama-cho, Ikoma, 630-0192, Japan, 81 743725250, wakamiya@is.naist.jp %K COVID-19 %K vaccine %K COVID-19 vaccine %K Pfizer %K Moderna %K vaccine side effects %K side effects %K Twitter %K logistic regression %D 2022 %7 4.10.2022 %9 Original Paper %J JMIR Infodemiology %G English %X Background: The year 2021 was marked by vaccinations against COVID-19, which spurred wider discussion among the general population, with some in favor and some against vaccination. Twitter, a popular social media platform, was instrumental in providing information about the COVID-19 vaccine and has been effective in observing public reactions. We focused on tweets from Japan and Indonesia, 2 countries with a large Twitter-using population, where concerns about side effects were consistently stated as a strong reason for vaccine hesitancy. Objective: This study aimed to investigate how Twitter was used to report vaccine-related side effects and to compare the mentions of these side effects from 2 messenger RNA (mRNA) vaccine types developed by Pfizer and Moderna, in Japan and Indonesia. Methods: We obtained tweet data from Twitter using Japanese and Indonesian keywords related to COVID-19 vaccines and their side effects from January 1, 2021, to December 31, 2021. We then removed users with a high frequency of tweets and merged the tweets from multiple users as a single sentence to focus on user-level analysis, resulting in a total of 214,165 users (Japan) and 12,289 users (Indonesia). Then, we filtered the data to select tweets mentioning Pfizer or Moderna only and removed tweets mentioning both. We compared the side effect counts to the public reports released by Pfizer and Moderna. Afterward, logistic regression models were used to compare the side effects for the Pfizer and Moderna vaccines for each country. Results: We observed some differences in the ratio of side effects between the public reports and tweets. Specifically, fever was mentioned much more frequently in tweets than would be expected based on the public reports. We also observed differences in side effects reported between Pfizer and Moderna vaccines from Japan and Indonesia, with more side effects reported for the Pfizer vaccine in Japanese tweets and more side effects with the Moderna vaccine reported in Indonesian tweets. Conclusions: We note the possible consequences of vaccine side effect surveillance on Twitter and information dissemination, in that fever appears to be over-represented. This could be due to fever possibly having a higher severity or measurability, and further implications are discussed. %M 36277140 %R 10.2196/39504 %U https://infodemiology.jmir.org/2022/2/e39504 %U https://doi.org/10.2196/39504 %U http://www.ncbi.nlm.nih.gov/pubmed/36277140 %0 Journal Article %@ 2564-1891 %I JMIR Publications %V 2 %N 2 %P e38839 %T Data Exploration and Classification of News Article Reliability: Deep Learning Study %A Zhan,Kevin %A Li,Yutong %A Osmani,Rafay %A Wang,Xiaoyu %A Cao,Bo %+ Department of Psychiatry, University of Alberta, 4-142 KATZ Group Centre for Pharmacy and Health Research, 87 Avenue and 114 Street, Edmonton, AB, T6G 2E1, Canada, 1 403 926 6628, yutong5@ualberta.ca %K COVID-19 %K deep learning %K news article reliability %K false information %K infodemic %K ensemble model %D 2022 %7 22.9.2022 %9 Original Paper %J JMIR Infodemiology %G English %X Background: During the ongoing COVID-19 pandemic, we are being exposed to large amounts of information each day. This “infodemic” is defined by the World Health Organization as the mass spread of misleading or false information during a pandemic. This spread of misinformation during the infodemic ultimately leads to misunderstandings of public health orders or direct opposition against public policies. Although there have been efforts to combat misinformation spread, current manual fact-checking methods are insufficient to combat the infodemic. Objective: We propose the use of natural language processing (NLP) and machine learning (ML) techniques to build a model that can be used to identify unreliable news articles online. Methods: First, we preprocessed the ReCOVery data set to obtain 2029 English news articles tagged with COVID-19 keywords from January to May 2020, which are labeled as reliable or unreliable. Data exploration was conducted to determine major differences between reliable and unreliable articles. We built an ensemble deep learning model using the body text, as well as features, such as sentiment, Empath-derived lexical categories, and readability, to classify the reliability. Results: We found that reliable news articles have a higher proportion of neutral sentiment, while unreliable articles have a higher proportion of negative sentiment. Additionally, our analysis demonstrated that reliable articles are easier to read than unreliable articles, in addition to having different lexical categories and keywords. Our new model was evaluated to achieve the following performance metrics: 0.906 area under the curve (AUC), 0.835 specificity, and 0.945 sensitivity. These values are above the baseline performance of the original ReCOVery model. Conclusions: This paper identified novel differences between reliable and unreliable news articles; moreover, the model was trained using state-of-the-art deep learning techniques. We aim to be able to use our findings to help researchers and the public audience more easily identify false information and unreliable media in their everyday lives. %M 36193330 %R 10.2196/38839 %U https://infodemiology.jmir.org/2022/2/e38839 %U https://doi.org/10.2196/38839 %U http://www.ncbi.nlm.nih.gov/pubmed/36193330 %0 Journal Article %@ 2564-1891 %I JMIR Publications %V 2 %N 2 %P e38573 %T The Information Sharing Behaviors of Dietitians and Twitter Users in the Nutrition and COVID-19 Infodemic: Content Analysis Study of Tweets %A Charbonneau,Esther %A Mellouli,Sehl %A Chouikh,Arbi %A Couture,Laurie-Jane %A Desroches,Sophie %+ Centre Nutrition, Santé et Société, Institute of Nutrition and Functional Foods, Université Laval, Pavillon des services, 2440 Hochelaga Blvd, Quebec City, QC, G1V 0A6, Canada, 1 418 656 2131 ext 405564, sophie.desroches@fsaa.ulaval.ca %K nutrition %K COVID-19 %K dietitians %K Twitter %K public %K themes %K behavior %K content accuracy %K user engagement %K content analysis %K misinformation %K disinformation %K infodemic %D 2022 %7 16.9.2022 %9 Original Paper %J JMIR Infodemiology %G English %X Background: The COVID-19 pandemic has generated an infodemic, an overabundance of online and offline information. In this context, accurate information as well as misinformation and disinformation about the links between nutrition and COVID-19 have circulated on Twitter since the onset of the pandemic. Objective: The purpose of this study was to compare tweets on nutrition in times of COVID-19 published by 2 groups, namely, a preidentified group of dietitians and a group of general users of Twitter, in terms of themes, content accuracy, use of behavior change factors, and user engagement, in order to contrast their information sharing behaviors during the pandemic. Methods: Public English-language tweets published between December 31, 2019, and December 31, 2020, by 625 dietitians from Canada and the United States, and Twitter users were collected using hashtags and keywords related to nutrition and COVID-19. After filtration, tweets were coded against an original codebook of themes and the Theoretical Domains Framework (TDF) for identifying behavior change factors, and were compared to reliable nutritional recommendations pertaining to COVID-19. The numbers of likes, replies, and retweets per tweet were also collected to determine user engagement. Results: In total, 2886 tweets (dietitians, n=1417; public, n=1469) were included in the analyses. Differences in frequency between groups were found in 11 out of 15 themes. Grocery (271/1417, 19.1%), and diets and dietary patterns (n=507, 34.5%) were the most frequently addressed themes by dietitians and the public, respectively. For 9 out of 14 TDF domains, there were differences in the frequency of usage between groups. “Skills” was the most used domain by both groups, although they used it in different proportions (dietitians: 612/1417, 43.2% vs public: 529/1469, 36.0%; P<.001). A higher proportion of dietitians’ tweets were accurate compared with the public’s tweets (532/575, 92.5% vs 250/382, 65.5%; P<.001). The results for user engagement were mixed. While engagement by likes varied between groups according to the theme, engagement by replies and retweets was similar across themes but varied according to the group. Conclusions: Differences in tweets between groups, notably ones related to content accuracy, themes, and engagement in the form of likes, shed light on potentially useful and relevant elements to include in timely social media interventions aiming at fighting the COVID-19–related infodemic or future infodemics. %M 36188421 %R 10.2196/38573 %U https://infodemiology.jmir.org/2022/2/e38573 %U https://doi.org/10.2196/38573 %U http://www.ncbi.nlm.nih.gov/pubmed/36188421 %0 Journal Article %@ 2564-1891 %I JMIR Publications %V 2 %N 2 %P e37635 %T Emotions and Incivility in Vaccine Mandate Discourse: Natural Language Processing Insights %A Stevens,Hannah %A Rasul,Muhammad Ehab %A Oh,Yoo Jung %+ University of California, Davis, 1 Shields Ave, Davis, CA, 95616, United States, 1 530 752 0966, hrstevens@ucdavis.edu %K vaccine hesitancy %K COVID-19 %K vaccine mandates %K natural language processing %K incivility %K LIWC %K Linguistic Inquiry and Word Count %K Twitter %D 2022 %7 13.9.2022 %9 Original Paper %J JMIR Infodemiology %G English %X Background: Despite vaccine availability, vaccine hesitancy has inhibited public health officials’ efforts to mitigate the COVID-19 pandemic in the United States. Although some US elected officials have responded by issuing vaccine mandates, others have amplified vaccine hesitancy by broadcasting messages that minimize vaccine efficacy. The politically polarized nature of COVID-19 information on social media has given rise to incivility, wherein health attitudes often hinge more on political ideology than science. Objective: To the best of our knowledge, incivility has not been studied in the context of discourse regarding COVID-19 vaccines and mandates. Specifically, there is little focus on the psychological processes that elicit uncivil vaccine discourse and behaviors. Thus, we investigated 3 psychological processes theorized to predict discourse incivility—namely, anxiety, anger, and sadness. Methods: We used 2 different natural language processing approaches: (1) the Linguistic Inquiry and Word Count computational tool and (2) the Google Perspective application programming interface (API) to analyze a data set of 8014 tweets containing terms related to COVID-19 vaccine mandates from September 14, 2021, to October 1, 2021. To collect the tweets, we used the Twitter API Tweet Downloader Tool (version 2). Subsequently, we filtered through a data set of 375,000 vaccine-related tweets using keywords to extract tweets explicitly focused on vaccine mandates. We relied on the Linguistic Inquiry and Word Count computational tool to measure the valence of linguistic anger, sadness, and anxiety in the tweets. To measure dimensions of post incivility, we used the Google Perspective API. Results: This study resolved discrepant operationalizations of incivility by introducing incivility as a multifaceted construct and explored the distinct emotional processes underlying 5 dimensions of discourse incivility. The findings revealed that 3 types of emotions—anxiety, anger, and sadness—were uniquely associated with dimensions of incivility (eg, toxicity, severe toxicity, insult, profanity, threat, and identity attacks). Specifically, the results showed that anger was significantly positively associated with all dimensions of incivility (all P<.001), whereas sadness was significantly positively related to threat (P=.04). Conversely, anxiety was significantly negatively associated with identity attack (P=.03) and profanity (P=.02). Conclusions: The results suggest that our multidimensional approach to incivility is a promising alternative to understanding and intervening in the psychological processes underlying uncivil vaccine discourse. Understanding specific emotions that can increase or decrease incivility such as anxiety, anger, and sadness can enable researchers and public health professionals to develop effective interventions against uncivil vaccine discourse. Given the need for real-time monitoring and automated responses to the spread of health information and misinformation on the web, social media platforms can harness the Google Perspective API to offer users immediate, automated feedback when it detects that a comment is uncivil. %M 36188420 %R 10.2196/37635 %U https://infodemiology.jmir.org/2022/2/e37635 %U https://doi.org/10.2196/37635 %U http://www.ncbi.nlm.nih.gov/pubmed/36188420 %0 Journal Article %@ 2564-1891 %I JMIR Publications %V 2 %N 2 %P e38756 %T COVID-19 Misinformation Detection: Machine-Learned Solutions to the Infodemic %A Kolluri,Nikhil %A Liu,Yunong %A Murthy,Dhiraj %+ Computational Media Lab, School of Journalism and Media, Moody College of Communication, The University of Texas at Austin, 300 W Dean Keeton (A0900), Austin, TX, 78712, United States, 1 512 471 5775, Dhiraj.Murthy@austin.utexas.edu %K COVID-19 %K misinformation %K machine learning %K fact-checking %K infodemiology %K infodemic management %K model performance %K model accuracy %K content analysis %D 2022 %7 25.8.2022 %9 Original Paper %J JMIR Infodemiology %G English %X Background: The volume of COVID-19–related misinformation has long exceeded the resources available to fact checkers to effectively mitigate its ill effects. Automated and web-based approaches can provide effective deterrents to online misinformation. Machine learning–based methods have achieved robust performance on text classification tasks, including potentially low-quality-news credibility assessment. Despite the progress of initial, rapid interventions, the enormity of COVID-19–related misinformation continues to overwhelm fact checkers. Therefore, improvement in automated and machine-learned methods for an infodemic response is urgently needed. Objective: The aim of this study was to achieve improvement in automated and machine-learned methods for an infodemic response. Methods: We evaluated three strategies for training a machine-learning model to determine the highest model performance: (1) COVID-19–related fact-checked data only, (2) general fact-checked data only, and (3) combined COVID-19 and general fact-checked data. We created two COVID-19–related misinformation data sets from fact-checked “false” content combined with programmatically retrieved “true” content. The first set contained ~7000 entries from July to August 2020, and the second contained ~31,000 entries from January 2020 to June 2022. We crowdsourced 31,441 votes to human label the first data set. Results: The models achieved an accuracy of 96.55% and 94.56% on the first and second external validation data set, respectively. Our best-performing model was developed using COVID-19–specific content. We were able to successfully develop combined models that outperformed human votes of misinformation. Specifically, when we blended our model predictions with human votes, the highest accuracy we achieved on the first external validation data set was 99.1%. When we considered outputs where the machine-learning model agreed with human votes, we achieved accuracies up to 98.59% on the first validation data set. This outperformed human votes alone with an accuracy of only 73%. Conclusions: External validation accuracies of 96.55% and 94.56% are evidence that machine learning can produce superior results for the difficult task of classifying the veracity of COVID-19 content. Pretrained language models performed best when fine-tuned on a topic-specific data set, while other models achieved their best accuracy when fine-tuned on a combination of topic-specific and general-topic data sets. Crucially, our study found that blended models, trained/fine-tuned on general-topic content with crowdsourced data, improved our models’ accuracies up to 99.7%. The successful use of crowdsourced data can increase the accuracy of models in situations when expert-labeled data are scarce. The 98.59% accuracy on a “high-confidence” subsection comprised of machine-learned and human labels suggests that crowdsourced votes can optimize machine-learned labels to improve accuracy above human-only levels. These results support the utility of supervised machine learning to deter and combat future health-related disinformation. %M 37113446 %R 10.2196/38756 %U https://infodemiology.jmir.org/2022/2/e38756 %U https://doi.org/10.2196/38756 %U http://www.ncbi.nlm.nih.gov/pubmed/37113446 %0 Journal Article %@ 2564-1891 %I JMIR Publications %V 2 %N 2 %P e37134 %T Infodemic Management Using Digital Information and Knowledge Cocreation to Address COVID-19 Vaccine Hesitancy: Case Study From Ghana %A Lohiniva,Anna-Leena %A Nurzhynska,Anastasiya %A Hudi,Al-hassan %A Anim,Bridget %A Aboagye,Da Costa %+ UNICEF Ghana Country Office, House No 4-6, Rangoon Ward No 24, Accra, Ghana, 233 549761859, Lohinivaa@gmail.com %K COVID-19 %K infodemic management %K misinformation %K disinformation %K social listening %K pandemic preparedness %K infodemiology %K social media %K Ghana %K vaccination %K qualitative methods %D 2022 %7 12.7.2022 %9 Original Paper %J JMIR Infodemiology %G English %X Background: Infodemic management is an integral part of pandemic management. Ghana Health Services (GHS) together with the UNICEF (United Nations International Children's Emergency Fund) Country Office have developed a systematic process that effectively identifies, analyzes, and responds to COVID-19 and vaccine-related misinformation in Ghana. Objective: This paper describes an infodemic management system workflow based on digital data collection, qualitative methodology, and human-centered systems to support the COVID-19 vaccine rollout in Ghana with examples of system implementation. Methods: The infodemic management system was developed by the Health Promotion Division of the GHS and the UNICEF Country Office. It uses Talkwalker, a social listening software platform, to collect misinformation on the web. The methodology relies on qualitative data analysis and interpretation as well as knowledge cocreation to verify the findings. Results: A multi-sectoral National Misinformation Task Force was established to implement and oversee the misinformation management system. Two members of the task force were responsible for carrying out the analysis. They used Talkwalker to find posts that include the keywords related to COVID-19 vaccine–related discussions. They then assessed the significance of the posts on the basis of the engagement rate and potential reach of the posts, negative sentiments, and contextual factors. The process continues by identifying misinformation within the posts, rating the risk of identified misinformation posts, and developing proposed responses to address them. The results of the analysis are shared weekly with the Misinformation Task Force for their review and verification to ensure that the risk assessment and responses are feasible, practical, and acceptable in the context of Ghana. Conclusions: The paper describes an infodemic management system workflow in Ghana based on qualitative data synthesis that can be used to manage real-time infodemic responses. %M 35854815 %R 10.2196/37134 %U https://infodemiology.jmir.org/2022/2/e37134 %U https://doi.org/10.2196/37134 %U http://www.ncbi.nlm.nih.gov/pubmed/35854815 %0 Journal Article %@ 2564-1891 %I JMIR Publications %V 2 %N 2 %P e38343 %T Social Listening to Enhance Access to Appropriate Pandemic Information Among Culturally Diverse Populations: Case Study From Finland %A Lohiniva,Anna-Leena %A Sibenberg,Katja %A Austero,Sara %A Skogberg,Natalia %+ Finnish Institute for Health and Welfare, 166 Mannerheimintie, Helsinki, 00300, Finland, 358 295247191, anna-leena.lohiniva@thl.fi %K infodemic %K social listening %K pandemic preparedness %K cultural diversity %K vulnerable populations %D 2022 %7 8.7.2022 %9 Original Paper %J JMIR Infodemiology %G English %X Background: Social listening, the process of monitoring and analyzing conversations to inform communication activities, is an essential component of infodemic management. It helps inform context-specific communication strategies that are culturally acceptable and appropriate for various subpopulations. Social listening is based on the notion that target audiences themselves can best define their own information needs and messages. Objective: This study aimed to describe the development of systematic social listening training for crisis communication and community outreach during the COVID-19 pandemic through a series of web-based workshops and to report the experiences of the workshop participants implementing the projects. Methods: A multidisciplinary team of experts developed a series of web-based training sessions for individuals responsible for community outreach or communication among linguistically diverse populations. The participants had no previous training in systematic data collection or monitoring. This training aimed to provide participants with sufficient knowledge and skills to develop a social listening system based on their specific needs and available resources. The workshop design took into consideration the pandemic context and focused on qualitative data collection. Information on the experiences of the participants in the training was gathered based on participant feedback and their assignments and through in-depth interviews with each team. Results: A series of 6 web-based workshops was conducted between May and September 2021. The workshops followed a systematic approach to social listening and included listening to web-based and offline sources; rapid qualitative analysis and synthesis; and developing communication recommendations, messages, and products. Follow-up meetings were organized between the workshops during which participants could share their achievements and challenges. Approximately 67% (4/6) of the participating teams established social listening systems by the end of the training. The teams tailored the knowledge provided during the training to their specific needs. As a result, the social systems developed by the teams had slightly different structures, target audiences, and aims. All resulting social listening systems followed the taught key principles of systematic social listening to collect and analyze data and used these new insights for further development of communication strategies. Conclusions: This paper describes an infodemic management system and workflow based on qualitative inquiry and adapted to local priorities and resources. The implementation of these projects resulted in content development for targeted risk communication, addressing linguistically diverse populations. These systems can be adapted for future epidemics and pandemics. %M 37113448 %R 10.2196/38343 %U https://infodemiology.jmir.org/2022/2/e38343 %U https://doi.org/10.2196/38343 %U http://www.ncbi.nlm.nih.gov/pubmed/37113448 %0 Journal Article %@ 2564-1891 %I JMIR Publications %V 2 %N 1 %P e34231 %T The Role of Influential Actors in Fostering the Polarized COVID-19 Vaccine Discourse on Twitter: Mixed Methods of Machine Learning and Inductive Coding %A Hagen,Loni %A Fox,Ashley %A O'Leary,Heather %A Dyson,DeAndre %A Walker,Kimberly %A Lengacher,Cecile A %A Hernandez,Raquel %+ School of Information, University of South Florida, 4202 E Fowler Ave, Tampa, FL, 33620, United States, 1 (813) 974 3520, lonihagen@usf.edu %K COVID-19, vaccine hesitancy, social media, influential actors %K influencer %K Twitter %D 2022 %7 30.6.2022 %9 Original Paper %J JMIR Infodemiology %G English %X Background: Since COVID-19 vaccines became broadly available to the adult population, sharp divergences in uptake have emerged along partisan lines. Researchers have indicated a polarized social media presence contributing to the spread of mis- or disinformation as being responsible for these growing partisan gaps in uptake. Objective: The major aim of this study was to investigate the role of influential actors in the context of the community structures and discourse related to COVID-19 vaccine conversations on Twitter that emerged prior to the vaccine rollout to the general population and discuss implications for vaccine promotion and policy. Methods: We collected tweets on COVID-19 between July 1, 2020, and July 31, 2020, a time when attitudes toward the vaccines were forming but before the vaccines were widely available to the public. Using network analysis, we identified different naturally emerging Twitter communities based on their internal information sharing. A PageRank algorithm was used to quantitively measure the level of “influentialness” of Twitter accounts and identifying the “influencers,” followed by coding them into different actor categories. Inductive coding was conducted to describe discourses shared in each of the 7 communities. Results: Twitter vaccine conversations were highly polarized, with different actors occupying separate “clusters.” The antivaccine cluster was the most densely connected group. Among the 100 most influential actors, medical experts were outnumbered both by partisan actors and by activist vaccine skeptics or conspiracy theorists. Scientists and medical actors were largely absent from the conservative network, and antivaccine sentiment was especially salient among actors on the political right. Conversations related to COVID-19 vaccines were highly polarized along partisan lines, with “trust” in vaccines being manipulated to the political advantage of partisan actors. Conclusions: These findings are informative for designing improved vaccine information communication strategies to be delivered on social media especially by incorporating influential actors. Although polarization and echo chamber effect are not new in political conversations in social media, it was concerning to observe these in health conversations on COVID-19 vaccines during the vaccine development process. %M 35814809 %R 10.2196/34231 %U https://infodemiology.jmir.org/2022/1/e34231 %U https://doi.org/10.2196/34231 %U http://www.ncbi.nlm.nih.gov/pubmed/35814809 %0 Journal Article %@ 2564-1891 %I JMIR Publications %V 2 %N 1 %P e37077 %T The Association Between Dissemination and Characteristics of Pro-/Anti-COVID-19 Vaccine Messages on Twitter: Application of the Elaboration Likelihood Model %A Saini,Vipin %A Liang,Li-Lin %A Yang,Yu-Chen %A Le,Huong Mai %A Wu,Chun-Ying %+ Institute of Public Health, College of Medicine, National Yang Ming Chiao Tung University, No 155, Sec 2, Linong St, Beitou Dist, Taipei, 112, Taiwan, 886 228267000 ext 67156, liang.lilin@nycu.edu.tw %K COVID-19 %K Twitter %K provaccine %K antivaccine %K elaboration likelihood model %K infodemiology %K dissemination %K content analysis %K emotional valence %K social media %D 2022 %7 27.6.2022 %9 Original Paper %J JMIR Infodemiology %G English %X Background: Messages on one’s stance toward vaccination on microblogging sites may affect the reader’s decision on whether to receive a vaccine. Understanding the dissemination of provaccine and antivaccine messages relating to COVID-19 on social media is crucial; however, studies on this topic have remained limited. Objective: This study applies the elaboration likelihood model (ELM) to explore the characteristics of vaccine stance messages that may appeal to Twitter users. First, we examined the associations between the characteristics of vaccine stance tweets and the likelihood and number of retweets. Second, we identified the relative importance of the central and peripheral routes in decision-making on sharing a message. Methods: English-language tweets from the United States that contained provaccine and antivaccine hashtags (N=150,338) were analyzed between April 26 and August 26, 2021. Logistic and generalized negative binomial regressions were conducted to predict retweet outcomes. The content-related central-route predictors were measured using the numbers of hashtags and mentions, emotional valence, emotional intensity, and concreteness. The content-unrelated peripheral-route predictors were measured using the numbers of likes and followers and whether the source was a verified user. Results: Content-related characteristics played a prominent role in shaping decisions regarding whether to retweet antivaccine messages. Particularly, positive valence (incidence rate ratio [IRR]=1.32, P=.03) and concreteness (odds ratio [OR]=1.17, P=.01) were associated with higher numbers and likelihood of retweets of antivaccine messages, respectively; emotional intensity (subjectivity) was associated with fewer retweets of antivaccine messages (OR=0.78, P=.03; IRR=0.80, P=.04). However, these factors had either no or only small effects on the sharing of provaccine tweets. Retweets of provaccine messages were primarily determined by content-unrelated characteristics, such as the numbers of likes (OR=2.55, IRR=2.24, P<.001) and followers (OR=1.31, IRR=1.28, P<.001). Conclusions: The dissemination of antivaccine messages is associated with both content-related and content-unrelated characteristics. By contrast, the dissemination of provaccine messages is primarily driven by content-unrelated characteristics. These findings signify the importance of leveraging the peripheral route to promote the dissemination of provaccine messages. Because antivaccine tweets with positive emotions, objective content, and concrete words are more likely to be disseminated, policymakers should pay attention to antivaccine messages with such characteristics. %M 35783451 %R 10.2196/37077 %U https://infodemiology.jmir.org/2022/1/e37077 %U https://doi.org/10.2196/37077 %U http://www.ncbi.nlm.nih.gov/pubmed/35783451 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 11 %N 6 %P e39133 %T Psychophysiological Reactions of Internet Users Exposed to Fluoride Information and Disinformation: Protocol for a Randomized Controlled Trial %A Lotto,Matheus %A Santana Jorge,Olivia %A Sá Menezes,Tamires %A Ramalho,Ana Maria %A Marchini Oliveira,Thais %A Bevilacqua,Fernando %A Cruvinel,Thiago %+ Department of Pediatric Dentistry, Orthodontics and Public Health, Bauru School of Dentistry, University of São Paulo, Al. Dr. Octávio Pinheiro Brisolla, 9-75, Vila Universitária, Bauru, 17012-901, Brazil, 55 14 3235 8318, thiagocruvinel@fob.usp.br %K fluoride %K disinformation %K randomized controlled trial %K social media %K internet %D 2022 %7 16.6.2022 %9 Protocol %J JMIR Res Protoc %G English %X Background: False messages on the internet continually propagate possible adverse effects of fluoridated oral care products and water, despite their essential role in preventing and controlling dental caries. Objective: This study aims to evaluate the patterns of psychophysiological reactions of adults after the consumption of internet-based fluoride-related information and disinformation. Methods: A 2-armed, single-blinded, parallel, and randomized controlled trial will be conducted with 58 parents or caregivers of children who attend the Clinics of Pediatric Dentistry at the Bauru School of Dentistry, considering an attrition of 10% and a significance level of 5%. The participants will be randomized into test and intervention groups, being respectively exposed to fluoride-related information and disinformation presented on a computer with simultaneous monitoring of their psychophysiological reactions, including analysis of their heart rates (HRs) and 7 facial features (mouth outer, mouth corner, eye area, eyebrow activity, face area, face motion, and facial center of mass). Then, participants will respond to questions about the utility and truthfulness of content, their emotional state after the experiment, eHealth literacy, oral health knowledge, and socioeconomic characteristics. The Shapiro-Wilk and Levene tests will be used to determine the normality and homogeneity of the data, which could lead to further statistical analyses for elucidating significant differences between groups, using parametric (Student t test) or nonparametric (Mann-Whitney U test) analyses. Moreover, multiple logistic regression models will be developed to evaluate the association of distinct variables with the psychophysiological aspects. Only factors with significant Wald statistics in the simple analysis will be included in the multiple models (P<.2). Furthermore, receiver operating characteristic curve analysis will be performed to determine the accuracy of the remote HR with respect to the measured HR. For all analyses, P<.05 will be considered significant. Results: From June 2022, parents and caregivers who frequent the Clinics of Pediatric Dentistry at the Bauru School of Dentistry will be invited to participate in the study and will be randomized into 1 of the 2 groups (control or intervention). Data collection is expected to be completed in December 2023. Subsequently, the authors will analyze the data and publish the findings of the clinical trial by June 2024. Conclusions: This randomized controlled trial aims to elucidate differences between psychophysiological patterns of adults exposed to true or false oral health content. This evidence may support the development of further studies and digital strategies, such as neural network models to automatically detect disinformation available on the internet. Trial Registration: Brazilian Clinical Trials Registry (RBR-7q4ymr2) U1111-1263-8227; https://tinyurl.com/2kf73t3d International Registered Report Identifier (IRRID): PRR1-10.2196/39133 %M 35708767 %R 10.2196/39133 %U https://www.researchprotocols.org/2022/6/e39133 %U https://doi.org/10.2196/39133 %U http://www.ncbi.nlm.nih.gov/pubmed/35708767 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 11 %N 5 %P e38034 %T A Web-Based Public Health Intervention for Addressing Vaccine Misinformation: Protocol for Analyzing Learner Engagement and Impacts on the Hesitancy to Vaccinate %A Powell,Leigh %A Nour,Radwa %A Zidoun,Youness %A Kaladhara,Sreelekshmi %A Al Suwaidi,Hanan %A Zary,Nabil %+ Institute for Excellence in Health Professions Education, Mohammed Bin Rashid University of Medicine and Health Sciences, Building 14, Dubai Healthcare City, PO Box 505055, Dubai, United Arab Emirates, 971 585960762, nabil.zary@icloud.com %K public health %K population health %K education %K gamification %K COVID-19 %K vaccination %K misinformation %K infodemic %K vaccine hesitancy %K web-based health %K web-based intervention %K learning design %K dissemination %D 2022 %7 30.5.2022 %9 Protocol %J JMIR Res Protoc %G English %X Background: A barrier to successful COVID-19 vaccine campaigns is the ongoing misinformation pandemic, or infodemic, which is contributing to vaccine hesitancy. Web-based population health interventions have been shown to impact health behaviors positively. For web-based interventions to be successful, they must use effective learning design strategies that seek to address known issues with learner engagement and retention. To know if an intervention successfully addresses vaccine hesitancy, there must be some embedded measure for comparing learners preintervention and postintervention. Objective: This protocol aims to describe a study on the effectiveness of a web-based population health intervention that is designed to address vaccine misinformation and hesitancy. The study will examine learner analytics to understand what aspects of the learning design for the intervention were effective and implement a validated instrument—the Adult Vaccine Hesitancy Scale—to measure if any changes in vaccine hesitancy were observed preintervention and postintervention. Methods: We developed a fully web-based population health intervention to help learners identify misinformation concerning COVID-19 and share the science behind vaccinations. Intervention development involves using a design-based research approach to output more effective interventions in which data can be analyzed to improve future health interventions. The study will use a quasi-experimental design in which a pre-post survey will be provided and compared statistically. Learning analytics will also be generated based on the engagement and retention data collected through the intervention to understand what aspects of our learning design are effective. Results: The web-based intervention was released to the public in September 2021, and data collection is ongoing. No external marketing or advertising has been done to market the course, making our current population of 486 participants our pilot study population. An analysis of this initial population will enable the revision of the intervention, which will then be marketed to a broader audience. Study outcomes are expected to be published by August 2022. We anticipate the release of the revised intervention by May 2022. Conclusions: Disseminating accurate information to the public during pandemic situations is vital to contributing to positive health outcomes, such as those among people getting vaccinated. Web-based interventions are valuable, as they can reach people anytime and anywhere. However, web-based interventions must use sound learning design to help incentivize engagement and motivate learners to learn and must provide a means of evaluating the intervention to determine its impact. Our study will examine both the learning design and the effectiveness of the intervention by using the analytics collected within the intervention and a statistical analysis of a validated instrument to determine if learners had a change in vaccine hesitancy as a result of what they learned. International Registered Report Identifier (IRRID): DERR1-10.2196/38034 %M 35451967 %R 10.2196/38034 %U https://www.researchprotocols.org/2022/5/e38034 %U https://doi.org/10.2196/38034 %U http://www.ncbi.nlm.nih.gov/pubmed/35451967 %0 Journal Article %@ 2564-1891 %I JMIR Publications %V 2 %N 1 %P e30167 %T Themes Surrounding COVID-19 and Its Infodemic: Qualitative Analysis of the COVID-19 Discussion on the Multidisciplinary Healthcare Information for All Health Forum %A Gangireddy,Rakshith %A Chakraborty,Stuti %A Pakenham-Walsh,Neil %A Nagarajan,Branavan %A Krishan,Prerna %A McGuire,Richard %A Vaghela,Gladson %A Sriharan,Abi %+ Institute of Health Policy, Management, and Evaluation, University of Toronto, 155 College St 4th Floor, Toronto, ON, M5T 3M6, Canada, 1 416 978 4326, abi.sriharan@utoronto.ca %K infodemic %K infodemiology %K COVID-19 %K pandemic %K misinformation %K health information %K theme %K public health %K qualitative study %K global health %D 2022 %7 11.5.2022 %9 Original Paper %J JMIR Infodemiology %G English %X Background: Healthcare Information for All (HIFA) is a multidisciplinary global campaign consisting of more than 20,000 members worldwide committed to improving the availability and use of health care information in low- and middle-income countries (LMICs). During the COVID-19 pandemic, online HIFA forums saw a tremendous amount of discussion regarding the lack of information about COVID-19, the spread of misinformation, and the pandemic’s impact on different communities. Objective: This study aims to analyze the themes and perspectives shared in the COVID-19 discussion on English HIFA forums. Methods: Over a period of 8 months, a qualitative thematic content analysis of the COVID-19 discussion on English HIFA forums was conducted. In total, 865 posts between January 24 and October 31, 2020, from 246 unique study participants were included and analyzed. Results: In total, 6 major themes were identified: infodemic, health system, digital health literacy, economic consequences, marginalized peoples, and mental health. The geographical distribution of study participants involved in the discussion spanned across 46 different countries in every continent except Antarctica. Study participants’ professions included public health workers, health care providers, and researchers, among others. Study participants’ affiliation included nongovernment organizations (NGOs), commercial organizations, academic institutions, the United Nations (UN), the World Health Organization (WHO), and others. Conclusions: The themes that emerged from this analysis highlight personal recounts, reflections, suggestions, and evidence around addressing COVID-19 related misinformation and might also help to understand the timeline of information evolution, focus, and needs surrounding the COVID-19 pandemic. %M 35586197 %R 10.2196/30167 %U https://infodemiology.jmir.org/2022/1/e30167 %U https://doi.org/10.2196/30167 %U http://www.ncbi.nlm.nih.gov/pubmed/35586197 %0 Journal Article %@ 2564-1891 %I JMIR Publications %V 2 %N 1 %P e32335 %T Public Opinion and Sentiment Before and at the Beginning of COVID-19 Vaccinations in Japan: Twitter Analysis %A Niu,Qian %A Liu,Junyu %A Kato,Masaya %A Shinohara,Yuki %A Matsumura,Natsuki %A Aoyama,Tomoki %A Nagai-Tanima,Momoko %+ Department of Human Health Sciences, Graduate School of Medicine, Kyoto University, 53, Kawahara-cho, Shogoin Sakyo-ku, Kyoto, 606-8507, Japan, 81 075 751 3964, tanima.momoko.8s@kyoto-u.ac.jp %K COVID-19 %K Japan %K vaccine %K Twitter %K sentiment %K latent dirichlet allocation %K natural language processing %D 2022 %7 9.5.2022 %9 Original Paper %J JMIR Infodemiology %G English %X Background: COVID-19 vaccines are considered one of the most effective ways for containing the COVID-19 pandemic, but Japan lagged behind other countries in vaccination in the early stages. A deeper understanding of the slow progress of vaccination in Japan can be instructive for COVID-19 booster vaccination and vaccinations during future pandemics. Objective: This retrospective study aims to analyze the slow progress of early-stage vaccination in Japan by exploring opinions and sentiment toward the COVID-19 vaccine in Japanese tweets before and at the beginning of vaccination. Methods: We collected 144,101 Japanese tweets containing COVID-19 vaccine-related keywords between August 1, 2020, and June 30, 2021. We visualized the trend of the tweets and sentiments and identified the critical events that may have triggered the surges. Correlations between sentiments and the daily infection, death, and vaccination cases were calculated. The latent dirichlet allocation model was applied to identify topics of negative tweets from the beginning of vaccination. We also conducted an analysis of vaccine brands (Pfizer, Moderna, AstraZeneca) approved in Japan. Results: The daily number of tweets continued with accelerating growth after the start of large-scale vaccinations in Japan. The sentiments of around 85% of the tweets were neutral, and negative sentiment overwhelmed the positive sentiment in the other tweets. We identified 6 public-concerned topics related to the negative sentiment at the beginning of the vaccination process. Among the vaccines from the 3 manufacturers, the attitude toward Moderna was the most positive, and the attitude toward AstraZeneca was the most negative. Conclusions: Negative sentiment toward vaccines dominated positive sentiment in Japan, and the concerns about side effects might have outweighed fears of infection at the beginning of the vaccination process. Topic modeling on negative tweets indicated that the government and policy makers should take prompt actions in building a safe and convenient vaccine reservation and rollout system, which requires both flexibility of the medical care system and the acceleration of digitalization in Japan. The public showed different attitudes toward vaccine brands. Policy makers should provide more evidence about the effectiveness and safety of vaccines and rebut fake news to build vaccine confidence. %M 35578643 %R 10.2196/32335 %U https://infodemiology.jmir.org/2022/1/e32335 %U https://doi.org/10.2196/32335 %U http://www.ncbi.nlm.nih.gov/pubmed/35578643 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 24 %N 4 %P e35788 %T Methods to Establish Race or Ethnicity of Twitter Users: Scoping Review %A Golder,Su %A Stevens,Robin %A O'Connor,Karen %A James,Richard %A Gonzalez-Hernandez,Graciela %+ Department of Health Sciences, University of York, Heslington, York, YO10 5DD, United Kingdom, 44 01904321904, su.golder@york.ac.uk %K twitter %K social media %K race %K ethnicity %D 2022 %7 29.4.2022 %9 Review %J J Med Internet Res %G English %X Background: A growing amount of health research uses social media data. Those critical of social media research often cite that it may be unrepresentative of the population; however, the suitability of social media data in digital epidemiology is more nuanced. Identifying the demographics of social media users can help establish representativeness. Objective: This study aims to identify the different approaches or combination of approaches to extract race or ethnicity from social media and report on the challenges of using these methods. Methods: We present a scoping review to identify methods used to extract the race or ethnicity of Twitter users from Twitter data sets. We searched 17 electronic databases from the date of inception to May 15, 2021, and carried out reference checking and hand searching to identify relevant studies. Sifting of each record was performed independently by at least two researchers, with any disagreement discussed. Studies were required to extract the race or ethnicity of Twitter users using either manual or computational methods or a combination of both. Results: Of the 1249 records sifted, we identified 67 (5.36%) that met our inclusion criteria. Most studies (51/67, 76%) have focused on US-based users and English language tweets (52/67, 78%). A range of data was used, including Twitter profile metadata, such as names, pictures, information from bios (including self-declarations), or location or content of the tweets. A range of methodologies was used, including manual inference, linkage to census data, commercial software, language or dialect recognition, or machine learning or natural language processing. However, not all studies have evaluated these methods. Those that evaluated these methods found accuracy to vary from 45% to 93% with significantly lower accuracy in identifying categories of people of color. The inference of race or ethnicity raises important ethical questions, which can be exacerbated by the data and methods used. The comparative accuracies of the different methods are also largely unknown. Conclusions: There is no standard accepted approach or current guidelines for extracting or inferring the race or ethnicity of Twitter users. Social media researchers must carefully interpret race or ethnicity and not overpromise what can be achieved, as even manual screening is a subjective, imperfect method. Future research should establish the accuracy of methods to inform evidence-based best practice guidelines for social media researchers and be guided by concerns of equity and social justice. %M 35486433 %R 10.2196/35788 %U https://www.jmir.org/2022/4/e35788 %U https://doi.org/10.2196/35788 %U http://www.ncbi.nlm.nih.gov/pubmed/35486433 %0 Journal Article %@ 2564-1891 %I JMIR Publications %V 2 %N 1 %P e33827 %T Identifying Frames of the COVID-19 Infodemic: Thematic Analysis of Misinformation Stories Across Media %A Mohammadi,Ehsan %A Tahamtan,Iman %A Mansourian,Yazdan %A Overton,Holly %+ School of Information Sciences, University of South Carolina, Davis College, Room 207, 1501 Greene Street, Columbia, SC, 29208, United States, 1 803 777 2324, ehsan2@sc.edu %K COVID-19 %K pandemic %K misinformation %K fake news %K framing theory %K social media %K infodemic %K thematic analysis %K theme %K social media %K pattern %K prevalence %D 2022 %7 13.4.2022 %9 Original Paper %J JMIR Infodemiology %G English %X Background: The word “infodemic” refers to the deluge of false information about an event, and it is a global challenge for today’s society. The sheer volume of misinformation circulating during the COVID-19 pandemic has been harmful to people around the world. Therefore, it is important to study different aspects of misinformation related to the pandemic. Objective: This paper aimed to identify the main subthemes related to COVID-19 misinformation on various platforms, from traditional outlets to social media. This paper aimed to place these subthemes into categories, track the changes, and explore patterns in prevalence, over time, across different platforms and contexts. Methods: From a theoretical perspective, this research was rooted in framing theory; it also employed thematic analysis to identify the main themes and subthemes related to COVID-19 misinformation. The data were collected from 8 fact-checking websites that formed a sample of 127 pieces of false COVID-19 news published from January 1, 2020 to March 30, 2020. Results: The findings revealed 4 main themes (attribution, impact, protection and solutions, and politics) and 19 unique subthemes within those themes related to COVID-19 misinformation. Governmental and political organizations (institutional level) and administrators and politicians (individual level) were the 2 most frequent subthemes, followed by origination and source, home remedies, fake statistics, treatments, drugs, and pseudoscience, among others. Results indicate that the prevalence of misinformation subthemes had altered over time between January 2020 and March 2020. For instance, false stories about the origin and source of the virus were frequent initially (January). Misinformation regarding home remedies became a prominent subtheme in the middle (February), while false information related to government organizations and politicians became popular later (March). Although conspiracy theory web pages and social media outlets were the primary sources of misinformation, surprisingly, results revealed trusted platforms such as official government outlets and news organizations were also avenues for creating COVID-19 misinformation. Conclusions: The identified themes in this study reflect some of the information attitudes and behaviors, such as denial, uncertainty, consequences, and solution-seeking, that provided rich information grounds to create different types of misinformation during the COVID-19 pandemic. Some themes also indicate that the application of effective communication strategies and the creation of timely content were used to persuade human minds with false stories in different phases of the crisis. The findings of this study can be beneficial for communication officers, information professionals, and policy makers to combat misinformation in future global health crises or related events. %M 37113806 %R 10.2196/33827 %U https://infodemiology.jmir.org/2022/1/e33827 %U https://doi.org/10.2196/33827 %U http://www.ncbi.nlm.nih.gov/pubmed/37113806 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 24 %N 3 %P e34050 %T Content Analysis of Nicotine Poisoning (Nic Sick) Videos on TikTok: Retrospective Observational Infodemiology Study %A Purushothaman,Vidya %A McMann,Tiana %A Nali,Matthew %A Li,Zhuoran %A Cuomo,Raphael %A Mackey,Tim K %+ Department of Anthropology, University of California San Diego, 9500 Gilman Drive, Postal Code: 0505, La Jolla, CA, 92093, United States, 1 9514914161, tmackey@ucsd.edu %K nic sick %K vaping %K tobacco %K social media %K TikTok %K content analysis %K smoking %K nicotine %K e-cigarette %K adverse effects %K public health %K infodemiology %D 2022 %7 30.3.2022 %9 Original Paper %J J Med Internet Res %G English %X Background: TikTok is a microvideo social media platform currently experiencing rapid growth and with 60% of its monthly users between the ages of 16 and 24 years. Increased exposure to e-cigarette content on social media may influence patterns of use, including the risk of overconsumption and possible nicotine poisoning, when users engage in trending challenges online. However, there is limited research assessing the characteristics of nicotine poisoning–related content posted on social media. Objective: We aimed to assess the characteristics of content on TikTok that is associated with a popular nicotine poisoning–related hashtag. Methods: We collected TikTok posts associated with the hashtag #nicsick, using a Python programming package (Selenium) and used an inductive coding approach to analyze video content and characteristics of interest. Videos were manually annotated to generate a codebook of the nicotine sickness–related themes. Statistical analysis was used to compare user engagement characteristics and video length in content with and without active nicotine sickness TikTok topics. Results: A total of 132 TikTok videos associated with the hashtag #nicsick were manually coded, with 52.3% (69/132) identified as discussing firsthand and secondhand reports of suspected nicotine poisoning symptoms and experiences. More than one-third of nicotine poisoning–related content (26/69, 37.68%) portrayed active vaping by users, which included content with vaping behavior such as vaping tricks and overconsumption, and 43% (30/69) of recorded users self-reported experiencing nicotine sickness, poisoning, or adverse events such as vomiting following nicotine consumption. The average follower count of users posting content related to nicotine sickness was significantly higher than that for users posting content unrelated to nicotine sickness (W=2350.5, P=.03). Conclusions: TikTok users openly discuss experiences, both firsthand and secondhand, with nicotine adverse events via the #nicsick hashtag including reports of overconsumption resulting in sickness. These study results suggest that there is a need to assess the utility of digital surveillance on emerging social media platforms for vaping adverse events, particularly on sites popular among youth and young adults. As vaping product use-patterns continue to evolve, digital adverse event detection likely represents an important tool to supplement traditional methods of public health surveillance (such as poison control center prevalence numbers). %M 35353056 %R 10.2196/34050 %U https://www.jmir.org/2022/3/e34050 %U https://doi.org/10.2196/34050 %U http://www.ncbi.nlm.nih.gov/pubmed/35353056 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 24 %N 3 %P e31088 %T Stratified Impacts of the Infodemic During the COVID-19 Pandemic: Cross-sectional Survey in 6 Asian Jurisdictions %A Chen,Xi %A Lin,Fen %A Cheng,Edmund W %+ Department of Media and Communication, City University of Hong Kong, M5086, 5/F, Run Run Shaw Creative Media Centre, 18 Tat Hong Avenue, Kowloon Tong, Hong Kong, 852 34428691, fenlin@cityu.edu.hk %K infodemic %K information overload %K psychological distress %K protective behavior %K cross-national survey %K Asia %K COVID-19 %D 2022 %7 22.3.2022 %9 Original Paper %J J Med Internet Res %G English %X Background: Although timely and accurate information during the COVID-19 pandemic is essential for containing the disease and reducing mental distress, an infodemic, which refers to an overabundance of information, may trigger unpleasant emotions and reduce compliance. Prior research has shown the negative consequences of an infodemic during the pandemic; however, we know less about which subpopulations are more exposed to the infodemic and are more vulnerable to the adverse psychological and behavioral effects. Objective: This study aimed to examine how sociodemographic factors and information-seeking behaviors affect the perceived information overload during the COVID-19 pandemic. We also investigated the effect of perceived information overload on psychological distress and protective behavior and analyzed the socioeconomic differences in the effects. Methods: The data for this study were obtained from a cross-national survey of residents in 6 jurisdictions in Asia in May 2020. The survey targeted residents aged 18 years or older. A probability-based quota sampling strategy was adopted to ensure that the selected samples matched the population’s geographical and demographic characteristics released by the latest available census in each jurisdiction. The final sample included 10,063 respondents. Information overload about COVID-19 was measured by asking the respondents to what extent they feel overwhelmed by news related to COVID-19. The measure of psychological distress was adapted from the Posttraumatic Stress Disorder Checklist for Diagnostic and Statistical Manual of Mental Disorders 5 (DSM-5). Protective behaviors included personal hygienic behavior and compliance with social distancing measures. Results: Younger respondents and women (b=0.20, 95% CI 0.14 to 0.26) were more likely to perceive information overload. Participants self-perceived as upper or upper-middle class (b=0.19, 95% CI 0.09 to 0.30) and those with full-time jobs (b=0.11, 95% CI 0.04 to 0.17) tended to perceive higher information overload. Respondents who more frequently sought COVID-19 information from newspapers (b=0.12, 95% CI 0.11 to 0.14), television (b=0.07, 95% CI 0.05 to 0.09), and family and friends (b=0.11, 95% CI 0.09 to 0.14) were more likely to feel overwhelmed. In contrast, obtaining COVID-19 information from online news outlets and social media was not associated with perceived information overload. There was a positive relationship between perceived information overload and psychological distress (b=2.18, 95% CI 2.09 to 2.26). Such an association was stronger among urban residents, full-time employees, and those living in privately owned housing. The effect of perceived information overload on protective behavior was not significant. Conclusions: Our findings revealed that respondents who were younger, were female, had a higher socioeconomic status (SES), and had vulnerable populations in the household were more likely to feel overwhelmed by COVID-19 information. Perceived information overload tended to increase psychological distress, and people with higher SES were more vulnerable to this adverse psychological consequence. Effective policies and interventions should be promoted to target vulnerable populations who are more susceptible to the occurrence and negative psychological influence of perceived information overload. %M 35103601 %R 10.2196/31088 %U https://www.jmir.org/2022/3/e31088 %U https://doi.org/10.2196/31088 %U http://www.ncbi.nlm.nih.gov/pubmed/35103601 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 24 %N 1 %P e28858 %T Harnessing Artificial Intelligence for Health Message Generation: The Folic Acid Message Engine %A Schmälzle,Ralf %A Wilcox,Shelby %+ Department of Communication, Michigan State University, 404 Wilson Rd, East Lansing, MI, 48824, United States, 1 (517) 353 ext 6629, schmaelz@msu.edu %K human-centered AI %K campaigns %K health communication %K NLP %K health promotion %D 2022 %7 18.1.2022 %9 Original Paper %J J Med Internet Res %G English %X Background: Communication campaigns using social media can raise public awareness; however, they are difficult to sustain. A barrier is the need to generate and constantly post novel but on-topic messages, which creates a resource-intensive bottleneck. Objective: In this study, we aim to harness the latest advances in artificial intelligence (AI) to build a pilot system that can generate many candidate messages, which could be used for a campaign to suggest novel, on-topic candidate messages. The issue of folic acid, a B-vitamin that helps prevent major birth defects, serves as an example; however, the system can work with other issues that could benefit from higher levels of public awareness. Methods: We used the Generative Pretrained Transformer-2 architecture, a machine learning model trained on a large natural language corpus, and fine-tuned it using a data set of autodownloaded tweets about #folicacid. The fine-tuned model was then used as a message engine, that is, to create new messages about this topic. We conducted a web-based study to gauge how human raters evaluate AI-generated tweet messages compared with original, human-crafted messages. Results: We found that the Folic Acid Message Engine can easily create several hundreds of new messages that appear natural to humans. Web-based raters evaluated the clarity and quality of a human-curated sample of AI-generated messages as on par with human-generated ones. Overall, these results showed that it is feasible to use such a message engine to suggest messages for web-based campaigns that focus on promoting awareness. Conclusions: The message engine can serve as a starting point for more sophisticated AI-guided message creation systems for health communication. Beyond the practical potential of such systems for campaigns in the age of social media, they also hold great scientific potential for the quantitative analysis of message characteristics that promote successful communication. We discuss future developments and obvious ethical challenges that need to be addressed as AI technologies for health persuasion enter the stage. %M 35040800 %R 10.2196/28858 %U https://www.jmir.org/2022/1/e28858 %U https://doi.org/10.2196/28858 %U http://www.ncbi.nlm.nih.gov/pubmed/35040800 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 7 %N 12 %P e31645 %T The Pandemic, Infodemic, and People’s Resilience in India: Viewpoint %A Syed Abdul,Shabbir %A Ramaswamy,Meghna %A Fernandez-Luque,Luis %A John,Oommen %A Pitti,Thejkiran %A Parashar,Babita %+ Graduate Institute of Biomedical Informatics, Taipei Medical University, No. 250, Wuxing Street, Xinyi District, Taipei, Taiwan, 886 02 2736 1661 ext 1514, drshabbir@tmu.edu.tw %K pandemic %K COVID-19 %K India %K digital health %K infodemics %K Sustainable Development Goals %K SDGs %D 2021 %7 8.12.2021 %9 Viewpoint %J JMIR Public Health Surveill %G English %X The COVID-19 pandemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has caused widespread fear and stress. The pandemic has affected everyone, everywhere, and created systemic inequities, leaving no one behind. In India alone, more than 34,094,373 confirmed COVID-19 cases and 452,454 related deaths have been reported as of October 19, 2021. Around May 2021, the daily number of new COVID-19 cases crossed the 400,000 mark, seriously hampering the health care system. Despite the devastating situation, the public response was seen through their efforts to come forward with innovative ideas for potential ways to combat the pandemic, for instance, dealing with the shortage of oxygen cylinders and hospital bed availability. With increasing COVID-19 vaccination rates since September 2021, along with the diminishing number of daily new cases, the country is conducting preventive and preparatory measures for the third wave. In this article, we propose the pivotal role of public participation and digital solutions to re-establish our society and describe how Sustainable Development Goals (SDGs) can support eHealth initiatives and mitigate infodemics to tackle a postpandemic situation. This viewpoint reflects that the COVID-19 pandemic has featured a need to bring together research findings across disciplines, build greater coherence within the field, and be a driving force for multi-sectoral, cross-disciplinary collaboration. The article also highlights the various needs to develop digital solutions that can be applied to pandemic situations and be reprocessed to focus on other SDGs. Promoting the use of digital health care solutions to implement preventive measures can be enhanced by public empowerment and engagement. Wearable technologies can be efficiently used for remote monitoring or home-based care for patients with chronic conditions. Furthermore, the development and implementation of informational tools can aid the improvement of well-being and dissolve panic-ridden behaviors contributing toward infodemics. Thus, a call to action for an observatory of digital health initiatives on COVID-19 is required to share the main conclusions and lessons learned in terms of resilience, crisis mitigation, and preparedness. %M 34787574 %R 10.2196/31645 %U https://publichealth.jmir.org/2021/12/e31645 %U https://doi.org/10.2196/31645 %U http://www.ncbi.nlm.nih.gov/pubmed/34787574 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 9 %N 11 %P e26065 %T Active Annotation in Evaluating the Credibility of Web-Based Medical Information: Guidelines for Creating Training Data Sets for Machine Learning %A Nabożny,Aleksandra %A Balcerzak,Bartłomiej %A Wierzbicki,Adam %A Morzy,Mikołaj %A Chlabicz,Małgorzata %+ Department of Software Engineering, Gdańsk University of Technology, 11/12 Gabriela Narutowicza St, Gdańsk, 80-233, Poland, 48 602327778, aleksandra.nabozny@pja.edu.pl %K active annotation %K credibility %K web-based medical information %K fake news %D 2021 %7 26.11.2021 %9 Original Paper %J JMIR Med Inform %G English %X Background: The spread of false medical information on the web is rapidly accelerating. Establishing the credibility of web-based medical information has become a pressing necessity. Machine learning offers a solution that, when properly deployed, can be an effective tool in fighting medical misinformation on the web. Objective: The aim of this study is to present a comprehensive framework for designing and curating machine learning training data sets for web-based medical information credibility assessment. We show how to construct the annotation process. Our main objective is to support researchers from the medical and computer science communities. We offer guidelines on the preparation of data sets for machine learning models that can fight medical misinformation. Methods: We begin by providing the annotation protocol for medical experts involved in medical sentence credibility evaluation. The protocol is based on a qualitative study of our experimental data. To address the problem of insufficient initial labels, we propose a preprocessing pipeline for the batch of sentences to be assessed. It consists of representation learning, clustering, and reranking. We call this process active annotation. Results: We collected more than 10,000 annotations of statements related to selected medical subjects (psychiatry, cholesterol, autism, antibiotics, vaccines, steroids, birth methods, and food allergy testing) for less than US $7000 by employing 9 highly qualified annotators (certified medical professionals), and we release this data set to the general public. We developed an active annotation framework for more efficient annotation of noncredible medical statements. The application of qualitative analysis resulted in a better annotation protocol for our future efforts in data set creation. Conclusions: The results of the qualitative analysis support our claims of the efficacy of the presented method. %M 34842547 %R 10.2196/26065 %U https://medinform.jmir.org/2021/11/e26065 %U https://doi.org/10.2196/26065 %U http://www.ncbi.nlm.nih.gov/pubmed/34842547 %0 Journal Article %@ 2562-0959 %I JMIR Publications %V 4 %N 2 %P e20975 %T Identifying and Responding to Health Misinformation on Reddit Dermatology Forums With Artificially Intelligent Bots Using Natural Language Processing: Design and Evaluation Study %A Sager,Monique A %A Kashyap,Aditya M %A Tamminga,Mila %A Ravoori,Sadhana %A Callison-Burch,Christopher %A Lipoff,Jules B %+ Department of Dermatology, University of Pennsylvania, 3737 Market Street, Suite 1100, Penn Medicine University City, Philadelphia, PA, 19104, United States, 1 215 662 8060, jules.lipoff@pennmedicine.upenn.edu %K bots %K natural language processing %K artificial intelligence %K Reddit, medical misinformation %K health misinformation %K detecting misinformation %K dermatology %K misinformation %D 2021 %7 30.9.2021 %9 Original Paper %J JMIR Dermatol %G English %X Background: Reddit, the fifth most popular website in the United States, boasts a large and engaged user base on its dermatology forums where users crowdsource free medical opinions. Unfortunately, much of the advice provided is unvalidated and could lead to the provision of inappropriate care. Initial testing has revealed that artificially intelligent bots can detect misinformation regarding tanning and essential oils on Reddit dermatology forums and may be able to produce responses to posts containing misinformation. Objective: To analyze the ability of bots to find and respond to tanning and essential oil–related health misinformation on Reddit’s dermatology forums in a controlled test environment. Methods: Using natural language processing techniques, we trained bots to target misinformation, using relevant keywords and to post prefabricated responses. By evaluating different model architectures across a held-out test set, we compared performances. Results: Our models yielded data test accuracies ranging 95%-100%, with a Bidirectional Encoder Representations from Transformers (BERT) fine-tuned model resulting in the highest level of test accuracy. Bots were then able to post corrective prefabricated responses to misinformation in a test environment. Conclusions: Using a limited data set, bots accurately detected examples of health misinformation within Reddit dermatology forums. Given that these bots can then post prefabricated responses, this technique may allow for interception of misinformation. Providing correct information does not mean that users will be receptive or find such interventions persuasive. Further studies should investigate this strategy’s effectiveness to inform future deployment of bots as a technique in combating health misinformation. %M 37632809 %R 10.2196/20975 %U https://derma.jmir.org/2021/2/e20975 %U https://doi.org/10.2196/20975 %U http://www.ncbi.nlm.nih.gov/pubmed/37632809 %0 Journal Article %@ 2564-1891 %I JMIR Publications %V 1 %N 1 %P e30979 %T A Public Health Research Agenda for Managing Infodemics: Methods and Results of the First WHO Infodemiology Conference %A Calleja,Neville %A AbdAllah,AbdelHalim %A Abad,Neetu %A Ahmed,Naglaa %A Albarracin,Dolores %A Altieri,Elena %A Anoko,Julienne N %A Arcos,Ruben %A Azlan,Arina Anis %A Bayer,Judit %A Bechmann,Anja %A Bezbaruah,Supriya %A Briand,Sylvie C %A Brooks,Ian %A Bucci,Lucie M %A Burzo,Stefano %A Czerniak,Christine %A De Domenico,Manlio %A Dunn,Adam G %A Ecker,Ullrich K H %A Espinosa,Laura %A Francois,Camille %A Gradon,Kacper %A Gruzd,Anatoliy %A Gülgün,Beste Sultan %A Haydarov,Rustam %A Hurley,Cherstyn %A Astuti,Santi Indra %A Ishizumi,Atsuyoshi %A Johnson,Neil %A Johnson Restrepo,Dylan %A Kajimoto,Masato %A Koyuncu,Aybüke %A Kulkarni,Shibani %A Lamichhane,Jaya %A Lewis,Rosamund %A Mahajan,Avichal %A Mandil,Ahmed %A McAweeney,Erin %A Messer,Melanie %A Moy,Wesley %A Ndumbi Ngamala,Patricia %A Nguyen,Tim %A Nunn,Mark %A Omer,Saad B %A Pagliari,Claudia %A Patel,Palak %A Phuong,Lynette %A Prybylski,Dimitri %A Rashidian,Arash %A Rempel,Emily %A Rubinelli,Sara %A Sacco,PierLuigi %A Schneider,Anton %A Shu,Kai %A Smith,Melanie %A Sufehmi,Harry %A Tangcharoensathien,Viroj %A Terry,Robert %A Thacker,Naveen %A Trewinnard,Tom %A Turner,Shannon %A Tworek,Heidi %A Uakkas,Saad %A Vraga,Emily %A Wardle,Claire %A Wasserman,Herman %A Wilhelm,Elisabeth %A Würz,Andrea %A Yau,Brian %A Zhou,Lei %A Purnat,Tina D %+ Department of Infectious Hazards Management, Emergency Preparedness Division, World Health Organization, Avenue Appia 20, Geneva, 1211, Switzerland, 41 22 791 21 11, nguyent@who.int %K infodemic %K infodemiology %K infodemic management %K research agenda %K research policy %K COVID-19 %K SARS-CoV-2 %K community resilience %K knowledge translation %K message amplification %K misinformation %K disinformation %K information-seeking behavior %K access to information %K information literacy %K communications media %K internet %K risk communication %K evidence synthesis %K attitudes %K beliefs %D 2021 %7 15.9.2021 %9 Original Paper %J JMIR Infodemiology %G English %X Background: An infodemic is an overflow of information of varying quality that surges across digital and physical environments during an acute public health event. It leads to confusion, risk-taking, and behaviors that can harm health and lead to erosion of trust in health authorities and public health responses. Owing to the global scale and high stakes of the health emergency, responding to the infodemic related to the pandemic is particularly urgent. Building on diverse research disciplines and expanding the discipline of infodemiology, more evidence-based interventions are needed to design infodemic management interventions and tools and implement them by health emergency responders. Objective: The World Health Organization organized the first global infodemiology conference, entirely online, during June and July 2020, with a follow-up process from August to October 2020, to review current multidisciplinary evidence, interventions, and practices that can be applied to the COVID-19 infodemic response. This resulted in the creation of a public health research agenda for managing infodemics. Methods: As part of the conference, a structured expert judgment synthesis method was used to formulate a public health research agenda. A total of 110 participants represented diverse scientific disciplines from over 35 countries and global public health implementing partners. The conference used a laddered discussion sprint methodology by rotating participant teams, and a managed follow-up process was used to assemble a research agenda based on the discussion and structured expert feedback. This resulted in a five-workstream frame of the research agenda for infodemic management and 166 suggested research questions. The participants then ranked the questions for feasibility and expected public health impact. The expert consensus was summarized in a public health research agenda that included a list of priority research questions. Results: The public health research agenda for infodemic management has five workstreams: (1) measuring and continuously monitoring the impact of infodemics during health emergencies; (2) detecting signals and understanding the spread and risk of infodemics; (3) responding and deploying interventions that mitigate and protect against infodemics and their harmful effects; (4) evaluating infodemic interventions and strengthening the resilience of individuals and communities to infodemics; and (5) promoting the development, adaptation, and application of interventions and toolkits for infodemic management. Each workstream identifies research questions and highlights 49 high priority research questions. Conclusions: Public health authorities need to develop, validate, implement, and adapt tools and interventions for managing infodemics in acute public health events in ways that are appropriate for their countries and contexts. Infodemiology provides a scientific foundation to make this possible. This research agenda proposes a structured framework for targeted investment for the scientific community, policy makers, implementing organizations, and other stakeholders to consider. %M 34604708 %R 10.2196/30979 %U https://infodemiology.jmir.org/2021/1/e30979 %U https://doi.org/10.2196/30979 %U http://www.ncbi.nlm.nih.gov/pubmed/34604708 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 23 %N 8 %P e28074 %T Association Between Public Opinion and Malaysian Government Communication Strategies About the COVID-19 Crisis: Content Analysis of Image Repair Strategies in Social Media %A Masngut,Nasaai %A Mohamad,Emma %+ Centre for Research in Media and Communication, Faculty of Social Sciences and Humanities, Universiti Kebangsaan Malaysia, Jalan Nik Ahmed Kamil, Bangi, 43600, Malaysia, 60 389215456, emmamohamad@ukm.edu.my %K COVID-19 %K crisis %K health communication %K image repair %K Malaysian government %K sentiment %K communication %K content analysis %K public opinion %K social media %K strategy %D 2021 %7 4.8.2021 %9 Original Paper %J J Med Internet Res %G English %X Background: The COVID-19 health crisis has posed an unprecedented challenge for governments worldwide to manage and communicate about the pandemic effectively, while maintaining public trust. Good leadership image in times of a health emergency is paramount to ensure public confidence in governments’ abilities to manage the crisis. Objective: The aim of this study was to identify types of image repair strategies utilized by the Malaysian government in their communication about COVID-19 in the media and analyze public responses to these messages on social media. Methods: Content analysis was employed to analyze 120 media statements and 382 comments retrieved from Facebook pages of 2 mainstream newspapers—Berita Harian and The Star. These media statements and comments were collected within a span of 6 weeks prior to and during the first implementation of Movement Control Order by the Malaysian Government. The media statements were analyzed according to Image Repair Theory to categorize strategies employed in government communications related to COVID-19 crisis. Public opinion responses were measured using modified lexicon-based sentiment analysis to categorize positive, negative, and neutral statements. Results: The Malaysian government employed all 5 Image Repair Theory strategies in their communications in both newspapers. The strategy most utilized was reducing offensiveness (75/120, 62.5%), followed by corrective action (30/120, 25.0%), evading responsibilities (10/120, 8.3%), denial (4/120, 3.3%), and mortification (1/120, 0.8%). This study also found multiple substrategies in government media statements including denial, shifting blame, provocation, defeasibility, accident, good intention, bolstering, minimization, differentiation, transcendence, attacking accuser, resolve problem, prevent recurrence, admit wrongdoing, and apologize. This study also found that 64.7% of public opinion was positive in response to media statements made by the Malaysian government and also revealed a significant positive association (P=.04) between image repair strategies utilized by the Malaysian government and public opinion. Conclusions: Communication in the media may assist the government in fostering positive support from the public. Suitable image repair strategies could garner positive public responses and help build trust in times of crisis. %M 34156967 %R 10.2196/28074 %U https://www.jmir.org/2021/8/e28074 %U https://doi.org/10.2196/28074 %U http://www.ncbi.nlm.nih.gov/pubmed/34156967 %0 Journal Article %@ 2564-1891 %I JMIR Publications %V 1 %N 1 %P e30971 %T Infodemic Signal Detection During the COVID-19 Pandemic: Development of a Methodology for Identifying Potential Information Voids in Online Conversations %A Purnat,Tina D %A Vacca,Paolo %A Czerniak,Christine %A Ball,Sarah %A Burzo,Stefano %A Zecchin,Tim %A Wright,Amy %A Bezbaruah,Supriya %A Tanggol,Faizza %A Dubé,Ève %A Labbé,Fabienne %A Dionne,Maude %A Lamichhane,Jaya %A Mahajan,Avichal %A Briand,Sylvie %A Nguyen,Tim %+ Emergency Preparedness, World Health Organization, 20 Avenue Appia, Geneva, 1211, Switzerland, 41 (0)227912111, czerniakc@who.int %K infodemic %K COVID-19 %K infodemic management %K social listening %K social monitoring %K social media %K pandemic preparedness %K pandemic response %K risk communication %K information voids %K data deficits %K information overload %D 2021 %7 28.7.2021 %9 Original Paper %J JMIR Infodemiology %G English %X Background: The COVID-19 pandemic has been accompanied by an infodemic: excess information, including false or misleading information, in digital and physical environments during an acute public health event. This infodemic is leading to confusion and risk-taking behaviors that can be harmful to health, as well as to mistrust in health authorities and public health responses. The World Health Organization (WHO) is working to develop tools to provide an evidence-based response to the infodemic, enabling prioritization of health response activities. Objective: In this work, we aimed to develop a practical, structured approach to identify narratives in public online conversations on social media platforms where concerns or confusion exist or where narratives are gaining traction, thus providing actionable data to help the WHO prioritize its response efforts to address the COVID-19 infodemic. Methods: We developed a taxonomy to filter global public conversations in English and French related to COVID-19 on social media into 5 categories with 35 subcategories. The taxonomy and its implementation were validated for retrieval precision and recall, and they were reviewed and adapted as language about the pandemic in online conversations changed over time. The aggregated data for each subcategory were analyzed on a weekly basis by volume, velocity, and presence of questions to detect signals of information voids with potential for confusion or where mis- or disinformation may thrive. A human analyst reviewed and identified potential information voids and sources of confusion, and quantitative data were used to provide insights on emerging narratives, influencers, and public reactions to COVID-19–related topics. Results: A COVID-19 public health social listening taxonomy was developed, validated, and applied to filter relevant content for more focused analysis. A weekly analysis of public online conversations since March 23, 2020, enabled quantification of shifting interests in public health–related topics concerning the pandemic, and the analysis demonstrated recurring voids of verified health information. This approach therefore focuses on the detection of infodemic signals to generate actionable insights to rapidly inform decision-making for a more targeted and adaptive response, including risk communication. Conclusions: This approach has been successfully applied to identify and analyze infodemic signals, particularly information voids, to inform the COVID-19 pandemic response. More broadly, the results have demonstrated the importance of ongoing monitoring and analysis of public online conversations, as information voids frequently recur and narratives shift over time. The approach is being piloted in individual countries and WHO regions to generate localized insights and actions; meanwhile, a pilot of an artificial intelligence–based social listening platform is using this taxonomy to aggregate and compare online conversations across 20 countries. Beyond the COVID-19 pandemic, the taxonomy and methodology may be adapted for fast deployment in future public health events, and they could form the basis of a routine social listening program for health preparedness and response planning. %M 34447926 %R 10.2196/30971 %U https://infodemiology.jmir.org/2021/1/e30971 %U https://doi.org/10.2196/30971 %U http://www.ncbi.nlm.nih.gov/pubmed/34447926 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 23 %N 5 %P e25714 %T Using a Secure, Continually Updating, Web Source Processing Pipeline to Support the Real-Time Data Synthesis and Analysis of Scientific Literature: Development and Validation Study %A Vaghela,Uddhav %A Rabinowicz,Simon %A Bratsos,Paris %A Martin,Guy %A Fritzilas,Epameinondas %A Markar,Sheraz %A Purkayastha,Sanjay %A Stringer,Karl %A Singh,Harshdeep %A Llewellyn,Charlie %A Dutta,Debabrata %A Clarke,Jonathan M %A Howard,Matthew %A , %A Serban,Ovidiu %A Kinross,James %+ Data Science Institute, Imperial College London, William Penney Laboratory, South Kensington Campus, London, United Kingdom, o.serban@imperial.ac.uk %K structured data synthesis %K data science %K critical analysis %K web crawl data %K pipeline %K database %K literature %K research %K COVID-19 %K infodemic %K decision making %K data %K data synthesis %K misinformation %K infrastructure %K methodology %D 2021 %7 6.5.2021 %9 Original Paper %J J Med Internet Res %G English %X Background: The scale and quality of the global scientific response to the COVID-19 pandemic have unquestionably saved lives. However, the COVID-19 pandemic has also triggered an unprecedented “infodemic”; the velocity and volume of data production have overwhelmed many key stakeholders such as clinicians and policy makers, as they have been unable to process structured and unstructured data for evidence-based decision making. Solutions that aim to alleviate this data synthesis–related challenge are unable to capture heterogeneous web data in real time for the production of concomitant answers and are not based on the high-quality information in responses to a free-text query. Objective: The main objective of this project is to build a generic, real-time, continuously updating curation platform that can support the data synthesis and analysis of a scientific literature framework. Our secondary objective is to validate this platform and the curation methodology for COVID-19–related medical literature by expanding the COVID-19 Open Research Dataset via the addition of new, unstructured data. Methods: To create an infrastructure that addresses our objectives, the PanSurg Collaborative at Imperial College London has developed a unique data pipeline based on a web crawler extraction methodology. This data pipeline uses a novel curation methodology that adopts a human-in-the-loop approach for the characterization of quality, relevance, and key evidence across a range of scientific literature sources. Results: REDASA (Realtime Data Synthesis and Analysis) is now one of the world’s largest and most up-to-date sources of COVID-19–related evidence; it consists of 104,000 documents. By capturing curators’ critical appraisal methodologies through the discrete labeling and rating of information, REDASA rapidly developed a foundational, pooled, data science data set of over 1400 articles in under 2 weeks. These articles provide COVID-19–related information and represent around 10% of all papers about COVID-19. Conclusions: This data set can act as ground truth for the future implementation of a live, automated systematic review. The three benefits of REDASA’s design are as follows: (1) it adopts a user-friendly, human-in-the-loop methodology by embedding an efficient, user-friendly curation platform into a natural language processing search engine; (2) it provides a curated data set in the JavaScript Object Notation format for experienced academic reviewers’ critical appraisal choices and decision-making methodologies; and (3) due to the wide scope and depth of its web crawling method, REDASA has already captured one of the world’s largest COVID-19–related data corpora for searches and curation. %M 33835932 %R 10.2196/25714 %U https://www.jmir.org/2021/5/e25714 %U https://doi.org/10.2196/25714 %U http://www.ncbi.nlm.nih.gov/pubmed/33835932 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 22 %N 6 %P e21820 %T How to Fight an Infodemic: The Four Pillars of Infodemic Management %A Eysenbach,Gunther %+ JMIR Publications, 130 Queens Quay East, Suite 1100, Toronto, ON, , Canada, 1 416 583 2040, editor@jmir.org %K infodemiology %K infodemic %K COVID-19 %K infoveillance %K pandemic %K epidemics %K emergency management %K public health %D 2020 %7 29.6.2020 %9 Commentary %J J Med Internet Res %G English %X In this issue of the Journal of Medical Internet Research, the World Health Organization (WHO) is presenting a framework for managing the coronavirus disease (COVID-19) infodemic. Infodemiology is now acknowledged by public health organizations and the WHO as an important emerging scientific field and critical area of practice during a pandemic. From the perspective of being the first “infodemiologist” who originally coined the term almost two decades ago, I am positing four pillars of infodemic management: (1) information monitoring (infoveillance); (2) building eHealth Literacy and science literacy capacity; (3) encouraging knowledge refinement and quality improvement processes such as fact checking and peer-review; and (4) accurate and timely knowledge translation, minimizing distorting factors such as political or commercial influences. In the current COVID-19 pandemic, the United Nations has advocated that facts and science should be promoted and that these constitute the antidote to the current infodemic. This is in stark contrast to the realities of infodemic mismanagement and misguided upstream filtering, where social media platforms such as Twitter have advertising policies that sideline science organizations and science publishers, treating peer-reviewed science as “inappropriate content.” %M 32589589 %R 10.2196/21820 %U http://www.jmir.org/2020/6/e21820/ %U https://doi.org/10.2196/21820 %U http://www.ncbi.nlm.nih.gov/pubmed/32589589 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 22 %N 6 %P e19659 %T Framework for Managing the COVID-19 Infodemic: Methods and Results of an Online, Crowdsourced WHO Technical Consultation %A Tangcharoensathien,Viroj %A Calleja,Neville %A Nguyen,Tim %A Purnat,Tina %A D’Agostino,Marcelo %A Garcia-Saiso,Sebastian %A Landry,Mark %A Rashidian,Arash %A Hamilton,Clayton %A AbdAllah,Abdelhalim %A Ghiga,Ioana %A Hill,Alexandra %A Hougendobler,Daniel %A van Andel,Judith %A Nunn,Mark %A Brooks,Ian %A Sacco,Pier Luigi %A De Domenico,Manlio %A Mai,Philip %A Gruzd,Anatoliy %A Alaphilippe,Alexandre %A Briand,Sylvie %+ Department of Digital Health and Innovation, Science Division, World Health Organization, 20 Avenue Appia, Geneva, 1211, Switzerland, 41 22 791 2476, purnatt@who.int %K COVID-19 %K infodemic %K knowledge translation %K message amplification %K misinformation %K information-seeking behavior %K access to information %K information literacy %K communications media %K internet %K risk communication %K evidence synthesis %D 2020 %7 26.6.2020 %9 Original Paper %J J Med Internet Res %G English %X Background: An infodemic is an overabundance of information—some accurate and some not—that occurs during an epidemic. In a similar manner to an epidemic, it spreads between humans via digital and physical information systems. It makes it hard for people to find trustworthy sources and reliable guidance when they need it. Objective: A World Health Organization (WHO) technical consultation on responding to the infodemic related to the coronavirus disease (COVID-19) pandemic was held, entirely online, to crowdsource suggested actions for a framework for infodemic management. Methods: A group of policy makers, public health professionals, researchers, students, and other concerned stakeholders was joined by representatives of the media, social media platforms, various private sector organizations, and civil society to suggest and discuss actions for all parts of society, and multiple related professional and scientific disciplines, methods, and technologies. A total of 594 ideas for actions were crowdsourced online during the discussions and consolidated into suggestions for an infodemic management framework. Results: The analysis team distilled the suggestions into a set of 50 proposed actions for a framework for managing infodemics in health emergencies. The consultation revealed six policy implications to consider. First, interventions and messages must be based on science and evidence, and must reach citizens and enable them to make informed decisions on how to protect themselves and their communities in a health emergency. Second, knowledge should be translated into actionable behavior-change messages, presented in ways that are understood by and accessible to all individuals in all parts of all societies. Third, governments should reach out to key communities to ensure their concerns and information needs are understood, tailoring advice and messages to address the audiences they represent. Fourth, to strengthen the analysis and amplification of information impact, strategic partnerships should be formed across all sectors, including but not limited to the social media and technology sectors, academia, and civil society. Fifth, health authorities should ensure that these actions are informed by reliable information that helps them understand the circulating narratives and changes in the flow of information, questions, and misinformation in communities. Sixth, following experiences to date in responding to the COVID-19 infodemic and the lessons from other disease outbreaks, infodemic management approaches should be further developed to support preparedness and response, and to inform risk mitigation, and be enhanced through data science and sociobehavioral and other research. Conclusions: The first version of this framework proposes five action areas in which WHO Member States and actors within society can apply, according to their mandate, an infodemic management approach adapted to national contexts and practices. Responses to the COVID-19 pandemic and the related infodemic require swift, regular, systematic, and coordinated action from multiple sectors of society and government. It remains crucial that we promote trusted information and fight misinformation, thereby helping save lives. %M 32558655 %R 10.2196/19659 %U http://www.jmir.org/2020/6/e19659/ %U https://doi.org/10.2196/19659 %U http://www.ncbi.nlm.nih.gov/pubmed/32558655