TY - JOUR AU - Yin, Dean-Chen Jason PY - 2022/8/10 TI - Media Data and Vaccine Hesitancy: Scoping Review JO - JMIR Infodemiology SP - e37300 VL - 2 IS - 2 KW - review KW - social media KW - traditional media KW - vaccine hesitancy KW - natural language processing KW - digital epidemiology N2 - Background: Media studies are important for vaccine hesitancy research, as they analyze how the media shapes risk perceptions and vaccine uptake. Despite the growth in studies in this field owing to advances in computing and language processing and an expanding social media landscape, no study has consolidated the methodological approaches used to study vaccine hesitancy. Synthesizing this information can better structure and set a precedent for this growing subfield of digital epidemiology. Objective: This review aimed to identify and illustrate the media platforms and methods used to study vaccine hesitancy and how they build or contribute to the study of the media?s influence on vaccine hesitancy and public health. Methods: This study followed the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines. A search was conducted on PubMed and Scopus for any studies that used media data (social media or traditional media), had an outcome related to vaccine sentiment (opinion, uptake, hesitancy, acceptance, or stance), were written in English, and were published after 2010. Studies were screened by only 1 reviewer and extracted for media platform, analysis method, the theoretical models used, and outcomes. Results: In total, 125 studies were included, of which 71 (56.8%) used traditional research methods and 54 (43.2%) used computational methods. Of the traditional methods, most used content analysis (43/71, 61%) and sentiment analysis (21/71, 30%) to analyze the texts. The most common platforms were newspapers, print media, and web-based news. The computational methods mostly used sentiment analysis (31/54, 57%), topic modeling (18/54, 33%), and network analysis (17/54, 31%). Fewer studies used projections (2/54, 4%) and feature extraction (1/54, 2%). The most common platforms were Twitter and Facebook. Theoretically, most studies were weak. The following five major categories of studies arose: antivaccination themes centered on the distrust of institutions, civil liberties, misinformation, conspiracy theories, and vaccine-specific concerns; provaccination themes centered on ensuring vaccine safety using scientific literature; framing being important and health professionals and personal stories having the largest impact on shaping vaccine opinion; the coverage of vaccination-related data mostly identifying negative vaccine content and revealing deeply fractured vaccine communities and echo chambers; and the public reacting to and focusing on certain signals?in particular cases, deaths, and scandals?which suggests a more volatile period for the spread of information. Conclusions: The heterogeneity in the use of media to study vaccines can be better consolidated through theoretical grounding. Areas of suggested research include understanding how trust in institutions is associated with vaccine uptake, how misinformation and information signaling influence vaccine uptake, and the evaluation of government communications on vaccine rollouts and vaccine-related events. The review ends with a statement that media data analyses, though groundbreaking in approach, should supplement?not supplant?current practices in public health research. UR - https://infodemiology.jmir.org/2022/2/e37300 UR - http://dx.doi.org/10.2196/37300 UR - http://www.ncbi.nlm.nih.gov/pubmed/37113443 ID - info:doi/10.2196/37300 ER - TY - JOUR AU - Lohiniva, Anna-Leena AU - Sibenberg, Katja AU - Austero, Sara AU - Skogberg, Natalia PY - 2022/7/8 TI - Social Listening to Enhance Access to Appropriate Pandemic Information Among Culturally Diverse Populations: Case Study From Finland JO - JMIR Infodemiology SP - e38343 VL - 2 IS - 2 KW - infodemic KW - social listening KW - pandemic preparedness KW - cultural diversity KW - vulnerable populations N2 - 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. UR - https://infodemiology.jmir.org/2022/2/e38343 UR - http://dx.doi.org/10.2196/38343 UR - http://www.ncbi.nlm.nih.gov/pubmed/37113448 ID - info:doi/10.2196/38343 ER - TY - JOUR AU - Li, Yachao AU - Ashley, L. David AU - Popova, Lucy PY - 2022/8/12 TI - Users? Modifications to Electronic Nicotine Delivery Systems: Content Analysis of YouTube Video Comments JO - JMIR Infodemiology SP - e38268 VL - 2 IS - 2 KW - ENDS modifications KW - YouTube KW - comments KW - vaping KW - content analysis N2 - Background: User modifications can alter the toxicity and addictiveness of electronic nicotine delivery systems (ENDSs). YouTube has been a major platform where ENDS users obtain and share information about ENDS modifications. Past research has examined the content and characteristics of ENDS modification videos. Objective: This study aims to analyze the video comments to understand the viewers? reactions to these videos. Methods: We identified 168 YouTube videos depicting ENDS modifications. Each video?s top 20 most liked comments were retrieved. The final sample included 2859 comments. A content analysis identified major themes of the comment content. Results: Most comments were directed to creators and interacted with others: 952/2859 (33.30%) expressed appreciation, 135/2859 (4.72%) requested more videos, 462/2859 (16.16%) asked for clarification, and 67/2859 (2.34%) inquired about product purchases. In addition, comments mentioned viewers? experiences of ENDS modifications (430/2859, 15.04%) and tobacco use (167/2859, 5.84%); about 198/2859 (6.93%) also indicated intentions to modify ENDSs and 34/2859 (1.19%) mentioned that they were ?newbies.? Moreover, comments included modification knowledge: 346/2859 (12.10%) provided additional information, 227/2859 (7.94%) mentioned newly learned knowledge, and 162/2859 (5.67%) criticized the videos. Furthermore, few comments mentioned the dangers of ENDS modifications (136/2859, 4.76%) and tobacco use (7/2859, 0.24%). Lastly, among the 15 comments explicitly mentioning regulations, 13/2859 (0.45%) were against and 2/2859 (0.07%) were supportive of regulations. Conclusions: The results indicated acceptance and popularity of ENDS modifications and suggested that the videos might motivate current and new users to alter their devices. Few comments mentioned the risks and regulations. Regulatory research and agencies should be aware of online ENDS modification information and understand its impacts on users. UR - https://infodemiology.jmir.org/2022/2/e38268 UR - http://dx.doi.org/10.2196/38268 UR - http://www.ncbi.nlm.nih.gov/pubmed/35992739 ID - info:doi/10.2196/38268 ER - TY - JOUR AU - Gillis, Timber AU - Garrison, Scott PY - 2022/7/19 TI - Confounding Effect of Undergraduate Semester?Driven ?Academic" Internet Searches on the Ability to Detect True Disease Seasonality in Google Trends Data: Fourier Filter Method Development and Demonstration JO - JMIR Infodemiology SP - e34464 VL - 2 IS - 2 KW - Google Trends KW - seasonality KW - Fast Fourier transform KW - FFT KW - pathogenic bacteria KW - depression KW - Google search KW - Google KW - health information KW - health information seeking KW - internet search N2 - Background: Internet search volume for medical information, as tracked by Google Trends, has been used to demonstrate unexpected seasonality in the symptom burden of a variety of medical conditions. However, when more technical medical language is used (eg, diagnoses), we believe that this technique is confounded by the cyclic, school year?driven internet search patterns of health care students. Objective: This study aimed to (1) demonstrate that artificial ?academic cycling? of Google Trends? search volume is present in many health care terms, (2) demonstrate how signal processing techniques can be used to filter academic cycling out of Google Trends data, and (3) apply this filtering technique to some clinically relevant examples. Methods: We obtained the Google Trends search volume data for a variety of academic terms demonstrating strong academic cycling and used a Fourier analysis technique to (1) identify the frequency domain fingerprint of this modulating pattern in one particularly strong example, and (2) filter that pattern out of the original data. After this illustrative example, we then applied the same filtering technique to internet searches for information on 3 medical conditions believed to have true seasonal modulation (myocardial infarction, hypertension, and depression), and all bacterial genus terms within a common medical microbiology textbook. Results: Academic cycling explains much of the seasonal variation in internet search volume for many technically oriented search terms, including the bacterial genus term [?Staphylococcus?], for which academic cycling explained 73.8% of the variability in search volume (using the squared Spearman rank correlation coefficient, P<.001). Of the 56 bacterial genus terms examined, 6 displayed sufficiently strong seasonality to warrant further examination post filtering. This included (1) [?Aeromonas? + ?Plesiomonas?] (nosocomial infections that were searched for more frequently during the summer), (2) [?Ehrlichia?] (a tick-borne pathogen that was searched for more frequently during late spring), (3) [?Moraxella?] and [?Haemophilus?] (respiratory infections that were searched for more frequently during late winter), (4) [?Legionella?] (searched for more frequently during midsummer), and (5) [?Vibrio?] (which spiked for 2 months during midsummer). The terms [?myocardial infarction?] and [?hypertension?] lacked any obvious seasonal cycling after filtering, whereas [?depression?] maintained an annual cycling pattern. Conclusions: Although it is reasonable to search for seasonal modulation of medical conditions using Google Trends? internet search volume and lay-appropriate search terms, the variation in more technical search terms may be driven by health care students whose search frequency varies with the academic school year. When this is the case, using Fourier analysis to filter out academic cycling is a potential means to establish whether additional seasonality is present. UR - https://infodemiology.jmir.org/2022/2/e34464 UR - http://dx.doi.org/10.2196/34464 UR - http://www.ncbi.nlm.nih.gov/pubmed/37113451 ID - info:doi/10.2196/34464 ER - TY - JOUR AU - Marcon, R. Alessandro AU - Wagner, N. Darren AU - Giles, Carly AU - Isenor, Cynthia PY - 2022/9/14 TI - Web-Based Perspectives of Deemed Consent Organ Donation Legislation in Nova Scotia: Thematic Analysis of Commentary in Facebook Groups JO - JMIR Infodemiology SP - e38242 VL - 2 IS - 2 KW - organ donation KW - organ transplantation KW - deemed consent KW - presumed consent KW - social media KW - Facebook KW - public perceptions KW - public policy KW - thematic analysis N2 - Background: The Canadian province of Nova Scotia recently became the first jurisdiction in North America to implement deemed consent organ donation legislation. Changing the consent models constituted one aspect of a larger provincial program to increase organ and tissue donation and transplantation rates. Deemed consent legislation can be controversial among the public, and public participation is integral to the successful implementation of the program. Objective: Social media constitutes key spaces where people express opinions and discuss topics, and social media discourse can influence public perceptions. This project aimed to examine how the public in Nova Scotia responded to legislative changes in Facebook groups. Methods: Using Facebook?s search engine, we searched for posts in public Facebook groups using the terms ?deemed consent,? ?presumed consent,? ?opt out,? or ?organ donation? and ?Nova Scotia,? appearing from January 1, 2020, to May 1, 2021. The finalized data set included 2337 comments on 26 relevant posts in 12 different public Nova Scotia?based Facebook groups. We conducted thematic and content analyses of the comments to determine how the public responded to the legislative changes and how the participants interacted with one another in the discussions. Results: Our thematic analysis revealed principal themes that supported and critiqued the legislation, raised specific issues, and reflected on the topic from a neutral perspective. Subthemes showed individuals presenting perspectives through a variety of themes, including compassion, anger, frustration, mistrust, and a range of argumentative tactics. The comments included personal narratives, beliefs about the government, altruism, autonomy, misinformation, and reflections on religion and death. Content analysis revealed that Facebook users reacted to popular comments with ?likes? more than other reactions. Comments with the most reactions included both negative and positive perspectives about the legislation. Personal donation and transplantation success stories, as well as attempts to correct misinformation, were some of the most ?liked? positive comments. Conclusions: The findings provide key insights into perspectives of individuals from Nova Scotia on deemed consent legislation, as well as organ donation and transplantation broadly. The insights derived from this analysis can contribute to public understanding, policy creation, and public outreach efforts that might occur in other jurisdictions considering the enactment of similar legislation. UR - https://infodemiology.jmir.org/2022/2/e38242 UR - http://dx.doi.org/10.2196/38242 UR - http://www.ncbi.nlm.nih.gov/pubmed/37113450 ID - info:doi/10.2196/38242 ER - TY - JOUR AU - Ezike, C. Nnamdi AU - Ames Boykin, Allison AU - Dobbs, D. Page AU - Mai, Huy AU - Primack, A. Brian PY - 2022/7/22 TI - Exploring Factors That Predict Marketing of e-Cigarette Products on Twitter: Infodemiology Approach Using Time Series JO - JMIR Infodemiology SP - e37412 VL - 2 IS - 2 KW - tobacco KW - electronic cigarettes KW - social media KW - marketing KW - time series KW - youth KW - young adults KW - infodemiology KW - infoveillance KW - digital marketing KW - advertising KW - Twitter KW - promote KW - e-cigarette N2 - Background: Electronic nicotine delivery systems (known as electronic cigarettes or e-cigarettes) increase risk for adverse health outcomes among naïve tobacco users, particularly youth and young adults. This vulnerable population is also at risk for exposed brand marketing and advertisement of e-cigarettes on social media. Understanding predictors of how e-cigarette manufacturers conduct social media advertising and marketing could benefit public health approaches to addressing e-cigarette use. Objective: This study documents factors that predict changes in daily frequency of commercial tweets about e-cigarettes using time series modeling techniques. Methods: We analyzed data on the daily frequency of commercial tweets about e-cigarettes collected between January 1, 2017, and December 31, 2020. We fit the data to an autoregressive integrated moving average (ARIMA) model and unobserved components model (UCM). Four measures assessed model prediction accuracy. Predictors in the UCM include days with events related to the US Food and Drug Administration (FDA), non-FDA-related events with significant importance such as academic or news announcements, weekday versus weekend, and the period when JUUL maintained an active Twitter account (ie, actively tweeting from their corporate Twitter account) versus when JUUL stopped tweeting. Results: When the 2 statistical models were fit to the data, the results indicate that the UCM was the best modeling technique for our data. All 4 predictors included in the UCM were significant predictors of the daily frequency of commercial tweets about e-cigarettes. On average, brand advertisement and marketing of e-cigarettes on Twitter was higher by more than 150 advertisements on days with FDA-related events compared to days without FDA events. Similarly, more than 40 commercial tweets about e-cigarettes were, on average, recorded on days with important non-FDA events compared to days without such events. We also found that there were more commercial tweets about e-cigarettes on weekdays than on weekends and more commercial tweets when JUUL maintained an active Twitter account. Conclusions: e-Cigarette companies promote their products on Twitter. Commercial tweets were significantly more likely to be posted on days with important FDA announcements, which may alter the narrative about information shared by the FDA. There remains a need for regulation of digital marketing of e-cigarette products in the United States. UR - https://infodemiology.jmir.org/2022/2/e37412 UR - http://dx.doi.org/10.2196/37412 UR - http://www.ncbi.nlm.nih.gov/pubmed/37113447 ID - info:doi/10.2196/37412 ER - TY - JOUR AU - Germone, Monique AU - Wright, D. Casey AU - Kimmons, Royce AU - Coburn, Skelley Shayna PY - 2022/12/5 TI - Twitter Trends for Celiac Disease and the Gluten-Free Diet: Cross-sectional Descriptive Analysis JO - JMIR Infodemiology SP - e37924 VL - 2 IS - 2 KW - celiac disease KW - social media KW - Twitter KW - gluten-free KW - social networking site KW - diet KW - infodemiology KW - education KW - online KW - content KW - accuracy KW - credibility N2 - Background: Few studies have systematically analyzed information regarding chronic medical conditions and available treatments on social media. Celiac disease (CD) is an exemplar of the need to investigate web-based educational sources. CD is an autoimmune condition wherein the ingestion of gluten causes intestinal damage and, if left untreated by a strict gluten-free diet (GFD), can result in significant nutritional deficiencies leading to cancer, bone disease, and death. Adherence to the GFD can be difficult owing to cost and negative stigma, including misinformation about what gluten is and who should avoid it. Given the significant impact that negative stigma and common misunderstandings have on the treatment of CD, this condition was chosen to systematically investigate the scope and nature of sources and information distributed through social media. Objective: To address concerns related to educational social media sources, this study explored trends on the social media platform Twitter about CD and the GFD to identify primary influencers and the type of information disseminated by these influencers. Methods: This cross-sectional study used data mining to collect tweets and users who used the hashtags #celiac and #glutenfree from an 8-month time frame. Tweets were then analyzed to describe who is disseminating information via this platform and the content, source, and frequency of such information. Results: More content was posted for #glutenfree (1501.8 tweets per day) than for #celiac (69 tweets per day). A substantial proportion of the content was produced by a small percentage of contributors (ie, ?Superuser?), who could be categorized as self-promotors (eg, bloggers, writers, authors; 13.9% of #glutenfree tweets and 22.7% of #celiac tweets), self-identified female family members (eg, mother; 4.3% of #glutenfree tweets and 8% of #celiac tweets), or commercial entities (eg, restaurants and bakeries). On the other hand, relatively few self-identified scientific, nonprofit, and medical provider users made substantial contributions on Twitter related to the GFD or CD (1% of #glutenfree tweets and 3.1% of #celiac tweets, respectively). Conclusions: Most material on Twitter was provided by self-promoters, commercial entities, or self-identified female family members, which may not have been supported by current medical and scientific practices. Researchers and medical providers could potentially benefit from contributing more to this space to enhance the web-based resources for patients and families. UR - https://infodemiology.jmir.org/2022/2/e37924 UR - http://dx.doi.org/10.2196/37924 UR - http://www.ncbi.nlm.nih.gov/pubmed/37113453 ID - info:doi/10.2196/37924 ER - TY - JOUR AU - Liu, Yongtai AU - Yin, Zhijun AU - Wan, Zhiyu AU - Yan, Chao AU - Xia, Weiyi AU - Ni, Congning AU - Clayton, Wright Ellen AU - Vorobeychik, Yevgeniy AU - Kantarcioglu, Murat AU - Malin, A. Bradley PY - 2022/8/3 TI - Implicit Incentives Among Reddit Users to Prioritize Attention Over Privacy and Reveal Their Faces When Discussing Direct-to-Consumer Genetic Test Results: Topic and Attention Analysis JO - JMIR Infodemiology SP - e35702 VL - 2 IS - 2 KW - direct-to-consumer genetic testing KW - topic modeling KW - social media N2 - Background: As direct-to-consumer genetic testing services have grown in popularity, the public has increasingly relied upon online forums to discuss and share their test results. Initially, users did so anonymously, but more recently, they have included face images when discussing their results. Various studies have shown that sharing images on social media tends to elicit more replies. However, users who do this forgo their privacy. When these images truthfully represent a user, they have the potential to disclose that user?s identity. Objective: This study investigates the face image sharing behavior of direct-to-consumer genetic testing users in an online environment to determine if there exists an association between face image sharing and the attention received from other users. Methods: This study focused on r/23andme, a subreddit dedicated to discussing direct-to-consumer genetic testing results and their implications. We applied natural language processing to infer the themes associated with posts that included a face image. We applied a regression analysis to characterize the association between the attention that a post received, in terms of the number of comments, the karma score (defined as the number of upvotes minus the number of downvotes), and whether the post contained a face image. Results: We collected over 15,000 posts from the r/23andme subreddit, published between 2012 and 2020. Face image posting began in late 2019 and grew rapidly, with over 800 individuals revealing their faces by early 2020. The topics in posts including a face were primarily about sharing, discussing ancestry composition, or sharing family reunion photos with relatives discovered via direct-to-consumer genetic testing. On average, posts including a face image received 60% (5/8) more comments and had karma scores 2.4 times higher than other posts. Conclusions: Direct-to-consumer genetic testing consumers in the r/23andme subreddit are increasingly posting face images and testing reports on social platforms. The association between face image posting and a greater level of attention suggests that people are forgoing their privacy in exchange for attention from others. To mitigate this risk, platform organizers and moderators could inform users about the risk of posting face images in a direct, explicit manner to make it clear that their privacy may be compromised if personal images are shared. UR - https://infodemiology.jmir.org/2022/2/e35702 UR - http://dx.doi.org/10.2196/35702 UR - http://www.ncbi.nlm.nih.gov/pubmed/37113452 ID - info:doi/10.2196/35702 ER - TY - JOUR AU - van Woudenberg, Thabo AU - Buijzen, Moniek AU - Hendrikx, Roy AU - van Weert, Julia AU - van den Putte, Bas AU - Kroese, Floor AU - Bouman, Martine AU - de Bruin, Marijn AU - Lambooij, Mattijs PY - 2022/8/11 TI - Physical Distancing and Social Media Use in Emerging Adults and Adults During the COVID-19 Pandemic: Large-scale Cross-sectional and Longitudinal Survey Study JO - JMIR Infodemiology SP - e33713 VL - 2 IS - 2 KW - COVID-19 KW - physical distancing KW - compliance KW - emerging adults KW - social media N2 - Background: Although emerging adults play a role in the spread of COVID-19, they are less likely to develop severe symptoms after infection. Emerging adults? relatively high use of social media as a source of information raises concerns regarding COVID-19?related behavioral compliance (ie, physical distancing) in this age group. Objective: This study aimed to investigate physical distancing among emerging adults in comparison with adults and examine the role of using social media for COVID-19 news and information in this regard. In addition, this study explored the relationship between physical distancing and using different social media platforms and sources. Methods: The secondary data of a large-scale longitudinal national survey (N=123,848) between April and November 2020 were used. Participants indicated, ranging from 1 to 8 waves, how often they were successful in keeping a 1.5-m distance on a 7-point Likert scale. Participants aged between 18 and 24 years were considered emerging adults, and those aged >24 years were considered adults. In addition, a dummy variable was created to indicate per wave whether participants used social media for COVID-19 news and information. A subset of participants received follow-up questions to determine which platforms they used and what sources of news and information they had seen on social media. All preregistered hypotheses were tested with linear mixed-effects models and random intercept cross-lagged panel models. Results: Emerging adults reported fewer physical distancing behaviors than adults (?=?.08, t86,213.83=?26.79; P<.001). Moreover, emerging adults were more likely to use social media for COVID-19 news and information (b=2.48; odds ratio 11.93 [95% CI=9.72-14.65]; SE 0.11; Wald=23.66; P<.001), which mediated the association with physical distancing but only to a small extent (indirect effect: b=?0.03, 95% CI ?0.04 to ?0.02). Contrary to our hypothesis, the longitudinal random intercept cross-lagged panel model showed no evidence that physical distancing was not influenced by social media use in the previous wave. However, evidence indicated that social media use affects subsequent physical distancing behavior. Moreover, additional analyses showed that the use of most social media platforms (ie, YouTube, Facebook, and Instagram) and interpersonal communication were negatively associated with physical distancing, whereas other platforms (ie, LinkedIn and Twitter) and government messages had no or small positive associations with physical distancing. Conclusions: In conclusion, we should be vigilant with regard to the physical distancing of emerging adults, but the study results did not indicate concerns regarding the role of social media for COVID-19 news and information. However, as the use of some social media platforms and sources showed negative associations with physical distancing, future studies should more carefully examine these factors to better understand the associations between social media use for news and information and behavioral interventions in times of crisis. UR - https://infodemiology.jmir.org/2022/2/e33713 UR - http://dx.doi.org/10.2196/33713 UR - http://www.ncbi.nlm.nih.gov/pubmed/35996459 ID - info:doi/10.2196/33713 ER - TY - JOUR AU - Buller, David AU - Walkosz, Barbara AU - Henry, Kimberly AU - Woodall, Gill W. AU - Pagoto, Sherry AU - Berteletti, Julia AU - Kinsey, Alishia AU - Divito, Joseph AU - Baker, Katie AU - Hillhouse, Joel PY - 2022/8/23 TI - Promoting Social Distancing and COVID-19 Vaccine Intentions to Mothers: Randomized Comparison of Information Sources in Social Media Messages JO - JMIR Infodemiology SP - e36210 VL - 2 IS - 2 KW - social media KW - COVID-19 KW - vaccination KW - nonpharmaceutical interventions KW - information source KW - misinformation KW - vaccine KW - public health KW - COVID-19 prevention KW - health promotion N2 - Background: Social media disseminated information and spread misinformation during the COVID-19 pandemic that affected prevention measures, including social distancing and vaccine acceptance. Objective: In this study, we aimed to test the effect of a series of social media posts promoting COVID-19 nonpharmaceutical interventions (NPIs) and vaccine intentions and compare effects among 3 common types of information sources: government agency, near-peer parents, and news media. Methods: A sample of mothers of teen daughters (N=303) recruited from a prior trial were enrolled in a 3 (information source) × 4 (assessment period) randomized factorial trial from January to March 2021 to evaluate the effects of information sources in a social media campaign addressing NPIs (ie, social distancing), COVID-19 vaccinations, media literacy, and mother?daughter communication about COVID-19. Mothers received 1 social media post per day in 3 randomly assigned Facebook private groups, Monday-Friday, covering all 4 topics each week, plus 1 additional post on a positive nonpandemic topic to promote engagement. Posts in the 3 groups had the same messages but differed by links to information from government agencies, near-peer parents, or news media in the post. Mothers reported on social distancing behavior and COVID-19 vaccine intentions for self and daughter, theoretic mediators, and covariates in baseline and 3-, 6-, and 9-week postrandomization assessments. Views, reactions, and comments related to each post were counted to measure engagement with the messages. Results: Nearly all mothers (n=298, 98.3%) remained in the Facebook private groups throughout the 9-week trial period, and follow-up rates were high (n=276, 91.1%, completed the 3-week posttest; n=273, 90.1%, completed the 6-week posttest; n=275, 90.8%, completed the 9-week posttest; and n=244, 80.5%, completed all assessments). In intent-to-treat analyses, social distancing behavior by mothers (b=?0.10, 95% CI ?0.12 to ?0.08, P<.001) and daughters (b=?0.10, 95% CI ?0.18 to ?0.03, P<.001) decreased over time but vaccine intentions increased (mothers: b=0.34, 95% CI 0.19-0.49, P<.001; daughters: b=0.17, 95% CI 0.04-0.29, P=.01). Decrease in social distancing by daughters was greater in the near-peer source group (b=?0.04, 95% CI ?0.07 to 0.00, P=.03) and lesser in the government agency group (b=0.05, 95% CI 0.02-0.09, P=.003). The higher perceived credibility of the assigned information source increased social distancing (mothers: b=0.29, 95% CI 0.09-0.49, P<.01; daughters: b=0.31, 95% CI 0.11-0.51, P<.01) and vaccine intentions (mothers: b=4.18, 95% CI 1.83-6.53, P<.001; daughters: b=3.36, 95% CI 1.67-5.04, P<.001). Mothers? intentions to vaccinate self may have increased when they considered the near-peer source to be not credible (b=?0.50, 95% CI ?0.99 to ?0.01, P=.05). Conclusions: Decreasing case counts, relaxation of government restrictions, and vaccine distribution during the study may explain the decreased social distancing and increased vaccine intentions. When promoting COVID-19 prevention, campaign planners may be more effective when selecting information sources that audiences consider credible, as no source was more credible in general. Trial Registration: ClinicalTrials.gov NCT02835807; https://clinicaltrials.gov/ct2/show/NCT02835807 UR - https://infodemiology.jmir.org/2022/2/e36210 UR - http://dx.doi.org/10.2196/36210 UR - http://www.ncbi.nlm.nih.gov/pubmed/36039372 ID - info:doi/10.2196/36210 ER - TY - JOUR AU - Toussaint, A. Philipp AU - Renner, Maximilian AU - Lins, Sebastian AU - Thiebes, Scott AU - Sunyaev, Ali PY - 2022/9/15 TI - Direct-to-Consumer Genetic Testing on Social Media: Topic Modeling and Sentiment Analysis of YouTube Users' Comments JO - JMIR Infodemiology SP - e38749 VL - 2 IS - 2 KW - direct-to-consumer genetic testing KW - health information KW - social media KW - YouTube KW - sentiment analysis KW - topic modeling KW - content analysis KW - online health information KW - user discourse KW - infodemiology N2 - Background: With direct-to-consumer (DTC) genetic testing enabling self-responsible access to novel information on ancestry, traits, or health, consumers often turn to social media for assistance and discussion. YouTube, the largest social media platform for videos, offers an abundance of DTC genetic testing?related videos. Nevertheless, user discourse in the comments sections of these videos is largely unexplored. Objective: This study aims to address the lack of knowledge concerning user discourse in the comments sections of DTC genetic testing?related videos on YouTube by exploring topics discussed and users' attitudes toward these videos. Methods: We employed a 3-step research approach. First, we collected metadata and comments of the 248 most viewed DTC genetic testing?related videos on YouTube. Second, we conducted topic modeling using word frequency analysis, bigram analysis, and structural topic modeling to identify topics discussed in the comments sections of those videos. Finally, we employed Bing (binary), National Research Council Canada (NRC) emotion, and 9-level sentiment analysis to identify users' attitudes toward these DTC genetic testing?related videos, as expressed in their comments. Results: We collected 84,082 comments from the 248 most viewed DTC genetic testing?related YouTube videos. With topic modeling, we identified 6 prevailing topics on (1) general genetic testing, (2) ancestry testing, (3) relationship testing, (4) health and trait testing, (5) ethical concerns, and (6) YouTube video reaction. Further, our sentiment analysis indicates strong positive emotions (anticipation, joy, surprise, and trust) and a neutral-to-positive attitude toward DTC genetic testing?related videos. Conclusions: With this study, we demonstrate how to identify users' attitudes on DTC genetic testing by examining topics and opinions based on YouTube video comments. Shedding light on user discourse on social media, our findings suggest that users are highly interested in DTC genetic testing and related social media content. Nonetheless, with this novel market constantly evolving, service providers, content providers, or regulatory authorities may still need to adapt their services to users' interests and desires. UR - https://infodemiology.jmir.org/2022/2/e38749 UR - http://dx.doi.org/10.2196/38749 UR - http://www.ncbi.nlm.nih.gov/pubmed/37113449 ID - info:doi/10.2196/38749 ER - TY - JOUR AU - Hu, Mengke AU - Conway, Mike PY - 2022/9/27 TI - Perspectives of the COVID-19 Pandemic on Reddit: Comparative Natural Language Processing Study of the United States, the United Kingdom, Canada, and Australia JO - JMIR Infodemiology SP - e36941 VL - 2 IS - 2 KW - COVID-19 KW - social media KW - natural language processing KW - Reddit N2 - Background: Since COVID-19 was declared a pandemic by the World Health Organization on March 11, 2020, the disease has had an unprecedented impact worldwide. Social media such as Reddit can serve as a resource for enhancing situational awareness, particularly regarding monitoring public attitudes and behavior during the crisis. Insights gained can then be utilized to better understand public attitudes and behaviors during the COVID-19 crisis, and to support communication and health-promotion messaging. Objective: The aim of this study was to compare public attitudes toward the 2020-2021 COVID-19 pandemic across four predominantly English-speaking countries (the United States, the United Kingdom, Canada, and Australia) using data derived from the social media platform Reddit. Methods: We utilized a topic modeling natural language processing method (more specifically latent Dirichlet allocation). Topic modeling is a popular unsupervised learning technique that can be used to automatically infer topics (ie, semantically related categories) from a large corpus of text. We derived our data from six country-specific, COVID-19?related subreddits (r/CoronavirusAustralia, r/CoronavirusDownunder, r/CoronavirusCanada, r/CanadaCoronavirus, r/CoronavirusUK, and r/coronavirusus). We used topic modeling methods to investigate and compare topics of concern for each country. Results: Our consolidated Reddit data set consisted of 84,229 initiating posts and 1,094,853 associated comments collected between February and November 2020 for the United States, the United Kingdom, Canada, and Australia. The volume of posting in COVID-19?related subreddits declined consistently across all four countries during the study period (February 2020 to November 2020). During lockdown events, the volume of posts peaked. The UK and Australian subreddits contained much more evidence-based policy discussion than the US or Canadian subreddits. Conclusions: This study provides evidence to support the contention that there are key differences between salient topics discussed across the four countries on the Reddit platform. Further, our approach indicates that Reddit data have the potential to provide insights not readily apparent in survey-based approaches. UR - https://infodemiology.jmir.org/2022/2/e36941 UR - http://dx.doi.org/10.2196/36941 UR - http://www.ncbi.nlm.nih.gov/pubmed/36196144 ID - info:doi/10.2196/36941 ER - TY - JOUR AU - Ferawati, Kiki AU - Liew, Kongmeng AU - Aramaki, Eiji AU - Wakamiya, Shoko PY - 2022/10/4 TI - Monitoring Mentions of COVID-19 Vaccine Side Effects on Japanese and Indonesian Twitter: Infodemiological Study JO - JMIR Infodemiology SP - e39504 VL - 2 IS - 2 KW - COVID-19 KW - vaccine KW - COVID-19 vaccine KW - Pfizer KW - Moderna KW - vaccine side effects KW - side effects KW - Twitter KW - logistic regression N2 - 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. UR - https://infodemiology.jmir.org/2022/2/e39504 UR - http://dx.doi.org/10.2196/39504 UR - http://www.ncbi.nlm.nih.gov/pubmed/36277140 ID - info:doi/10.2196/39504 ER - TY - JOUR AU - van Kampen, Katherine AU - Laski, Jeremi AU - Herman, Gabrielle AU - Chan, M. Teresa PY - 2022/10/25 TI - Investigating COVID-19 Vaccine Communication and Misinformation on TikTok: Cross-sectional Study JO - JMIR Infodemiology SP - e38316 VL - 2 IS - 2 KW - TikTok KW - COVID-19 vaccines KW - vaccinations KW - misinformation KW - COVID-19 KW - Infodemiology KW - social media KW - health information KW - content analysis KW - vaccine hesitancy KW - public health KW - web-based health information N2 - Background: The COVID-19 pandemic has highlighted the need for reliable information, especially around vaccines. Vaccine hesitancy is a growing concern and a great threat to broader public health. The prevalence of social media within our daily lives emphasizes the importance of accurately analyzing how health information is being disseminated to the public. TikTok is of particular interest, as it is an emerging social media platform that young adults may be increasingly using to access health information. Objective: The objective of this study was to examine and describe the content within the top 100 TikToks trending with the hashtag #covidvaccine. Methods: The top 250 most viewed TikToks with the hashtag #covidvaccine were batch downloaded on July 1, 2021, with their respective metadata. Each TikTok was subsequently viewed and encoded by 2 independent reviewers. Coding continued until 100 TikToks could be included based on language and content. Descriptive features were recorded including health care professional (HCP) status of creator, verification of HCP status, genre, and misinformation addressed. Primary inclusion criteria were any TikToks in English with discussion of a COVID-19 vaccine. Results: Of 102 videos included, the median number of plays was 1,700,000, with median shares of 9224 and 62,200 followers. Upon analysis, 14.7% (15/102) of TikToks included HCPs, of which 80% (12/102) could be verified via social media or regulatory body search; 100% (15/15) of HCP-created TikToks supported vaccine use, and overall, 81.3% (83/102) of all TikToks (created by either a layperson or an HCP) supported vaccine use. Conclusions: As the pandemic continues, vaccine hesitancy poses a threat to lifting restrictions, and discovering reasons for this hesitancy is important to public health measures. This study summarizes the discourse around vaccine use on TikTok. Importantly, it opens a frank discussion about the necessity to incorporate new social media platforms into medical education, so we might ensure our trainees are ready to engage with patients on novel platforms. UR - https://infodemiology.jmir.org/2022/2/e38316 UR - http://dx.doi.org/10.2196/38316 UR - http://www.ncbi.nlm.nih.gov/pubmed/36338548 ID - info:doi/10.2196/38316 ER - TY - JOUR AU - Ke, Yang Si AU - Neeley-Tass, Shannon E. AU - Barnes, Michael AU - Hanson, L. Carl AU - Giraud-Carrier, Christophe AU - Snell, Quinn PY - 2022/10/31 TI - COVID-19 Health Beliefs Regarding Mask Wearing and Vaccinations on Twitter: Deep Learning Approach JO - JMIR Infodemiology SP - e37861 VL - 2 IS - 2 KW - COVID-19 KW - Health Belief Model KW - deep learning KW - mask KW - vaccination KW - machine learning KW - vaccine data set KW - Twitter KW - content analysis KW - infodemic KW - infodemiology KW - misinformation KW - health belief N2 - Background: Amid the global COVID-19 pandemic, a worldwide infodemic also emerged with large amounts of COVID-19?related information and misinformation spreading through social media channels. Various organizations, including the World Health Organization (WHO) and the Centers for Disease Control and Prevention (CDC), and other prominent individuals issued high-profile advice on preventing the further spread of COVID-19. Objective: The purpose of this study is to leverage machine learning and Twitter data from the pandemic period to explore health beliefs regarding mask wearing and vaccines and the influence of high-profile cues to action. Methods: A total of 646,885,238 COVID-19?related English tweets were filtered, creating a mask-wearing data set and a vaccine data set. Researchers manually categorized a training sample of 3500 tweets for each data set according to their relevance to Health Belief Model (HBM) constructs and used coded tweets to train machine learning models for classifying each tweet in the data sets. Results: In total, 5 models were trained for both the mask-related and vaccine-related data sets using the XLNet transformer model, with each model achieving at least 81% classification accuracy. Health beliefs regarding perceived benefits and barriers were most pronounced for both mask wearing and immunization; however, the strength of those beliefs appeared to vary in response to high-profile cues to action. Conclusions: During both the COVID-19 pandemic and the infodemic, health beliefs related to perceived benefits and barriers observed through Twitter using a big data machine learning approach varied over time and in response to high-profile cues to action from prominent organizations and individuals. UR - https://infodemiology.jmir.org/2022/2/e37861 UR - http://dx.doi.org/10.2196/37861 UR - http://www.ncbi.nlm.nih.gov/pubmed/36348979 ID - info:doi/10.2196/37861 ER - TY - JOUR AU - Erturk, Sinan AU - Hudson, Georgie AU - Jansli, M. Sonja AU - Morris, Daniel AU - Odoi, M. Clarissa AU - Wilson, Emma AU - Clayton-Turner, Angela AU - Bray, Vanessa AU - Yourston, Gill AU - Cornwall, Andrew AU - Cummins, Nicholas AU - Wykes, Til AU - Jilka, Sagar PY - 2022/11/22 TI - Codeveloping and Evaluating a Campaign to Reduce Dementia Misconceptions on Twitter: Machine Learning Study JO - JMIR Infodemiology SP - e36871 VL - 2 IS - 2 KW - machine learning KW - patient and public involvement KW - codevelopment KW - misconceptions KW - stigma KW - Twitter KW - social media N2 - Background: Dementia misconceptions on Twitter can have detrimental or harmful effects. Machine learning (ML) models codeveloped with carers provide a method to identify these and help in evaluating awareness campaigns. Objective: This study aimed to develop an ML model to distinguish between misconceptions and neutral tweets and to develop, deploy, and evaluate an awareness campaign to tackle dementia misconceptions. Methods: Taking 1414 tweets rated by carers from our previous work, we built 4 ML models. Using a 5-fold cross-validation, we evaluated them and performed a further blind validation with carers for the best 2 ML models; from this blind validation, we selected the best model overall. We codeveloped an awareness campaign and collected pre-post campaign tweets (N=4880), classifying them with our model as misconceptions or not. We analyzed dementia tweets from the United Kingdom across the campaign period (N=7124) to investigate how current events influenced misconception prevalence during this time. Results: A random forest model best identified misconceptions with an accuracy of 82% from blind validation and found that 37% of the UK tweets (N=7124) about dementia across the campaign period were misconceptions. From this, we could track how the prevalence of misconceptions changed in response to top news stories in the United Kingdom. Misconceptions significantly rose around political topics and were highest (22/28, 79% of the dementia tweets) when there was controversy over the UK government allowing to continue hunting during the COVID-19 pandemic. After our campaign, there was no significant change in the prevalence of misconceptions. Conclusions: Through codevelopment with carers, we developed an accurate ML model to predict misconceptions in dementia tweets. Our awareness campaign was ineffective, but similar campaigns could be enhanced through ML to respond to current events that affect misconceptions in real time. UR - https://infodemiology.jmir.org/2022/2/e36871 UR - http://dx.doi.org/10.2196/36871 UR - http://www.ncbi.nlm.nih.gov/pubmed/37113444 ID - info:doi/10.2196/36871 ER - TY - JOUR AU - Déguilhem, Amélia AU - Malaab, Joelle AU - Talmatkadi, Manissa AU - Renner, Simon AU - Foulquié, Pierre AU - Fagherazzi, Guy AU - Loussikian, Paul AU - Marty, Tom AU - Mebarki, Adel AU - Texier, Nathalie AU - Schuck, Stephane PY - 2022/11/22 TI - Identifying Profiles and Symptoms of Patients With Long COVID in France: Data Mining Infodemiology Study Based on Social Media JO - JMIR Infodemiology SP - e39849 VL - 2 IS - 2 KW - long COVID KW - social media KW - Long Haulers KW - difficulties encountered KW - symptoms KW - infodemiology study KW - infodemiology KW - COVID-19 KW - patient-reported outcomes KW - persistent KW - condition KW - topics KW - discussion KW - content N2 - Background: Long COVID?a condition with persistent symptoms post COVID-19 infection?is the first illness arising from social media. In France, the French hashtag #ApresJ20 described symptoms persisting longer than 20 days after contracting COVID-19. Faced with a lack of recognition from medical and official entities, patients formed communities on social media and described their symptoms as long-lasting, fluctuating, and multisystemic. While many studies on long COVID relied on traditional research methods with lengthy processes, social media offers a foundation for large-scale studies with a fast-flowing outburst of data. Objective: We aimed to identify and analyze Long Haulers? main reported symptoms, symptom co-occurrences, topics of discussion, difficulties encountered, and patient profiles. Methods: Data were extracted based on a list of pertinent keywords from public sites (eg, Twitter) and health-related forums (eg, Doctissimo). Reported symptoms were identified via the MedDRA dictionary, displayed per the volume of posts mentioning them, and aggregated at the user level. Associations were assessed by computing co-occurrences in users? messages, as pairs of preferred terms. Discussion topics were analyzed using the Biterm Topic Modeling; difficulties and unmet needs were explored manually. To identify patient profiles in relation to their symptoms, each preferred term?s total was used to create user-level hierarchal clusters. Results: Between January 1, 2020, and August 10, 2021, overall, 15,364 messages were identified as originating from 6494 patients of long COVID or their caregivers. Our analyses revealed 3 major symptom co-occurrences: asthenia-dyspnea (102/289, 35.3%), asthenia-anxiety (65/289, 22.5%), and asthenia-headaches (50/289, 17.3%). The main reported difficulties were symptom management (150/424, 35.4% of messages), psychological impact (64/424,15.1%), significant pain (51/424, 12.0%), deterioration in general well-being (52/424, 12.3%), and impact on daily and professional life (40/424, 9.4% and 34/424, 8.0% of messages, respectively). We identified 3 profiles of patients in relation to their symptoms: profile A (n=406 patients) reported exclusively an asthenia symptom; profile B (n=129) expressed anxiety (n=129, 100%), asthenia (n=28, 21.7%), dyspnea (n=15, 11.6%), and ageusia (n=3, 2.3%); and profile C (n=141) described dyspnea (n=141, 100%), and asthenia (n=45, 31.9%). Approximately 49.1% of users (79/161) continued expressing symptoms after more than 3 months post infection, and 20.5% (33/161) after 1 year. Conclusions: Long COVID is a lingering condition that affects people worldwide, physically and psychologically. It impacts Long Haulers? quality of life, everyday tasks, and professional activities. Social media played an undeniable role in raising and delivering Long Haulers? voices and can potentially rapidly provide large volumes of valuable patient-reported information. Since long COVID was a self-titled condition by patients themselves via social media, it is imperative to continuously include their perspectives in related research. Our results can help design patient-centric instruments to be further used in clinical practice to better capture meaningful dimensions of long COVID. UR - https://infodemiology.jmir.org/2022/2/e39849 UR - http://dx.doi.org/10.2196/39849 UR - http://www.ncbi.nlm.nih.gov/pubmed/36447795 ID - info:doi/10.2196/39849 ER - TY - JOUR AU - Lohiniva, Anna-Leena AU - Nurzhynska, Anastasiya AU - Hudi, Al-hassan AU - Anim, Bridget AU - Aboagye, Costa Da PY - 2022/7/12 TI - Infodemic Management Using Digital Information and Knowledge Cocreation to Address COVID-19 Vaccine Hesitancy: Case Study From Ghana JO - JMIR Infodemiology SP - e37134 VL - 2 IS - 2 KW - COVID-19 KW - infodemic management KW - misinformation KW - disinformation KW - social listening KW - pandemic preparedness KW - infodemiology KW - social media KW - Ghana KW - vaccination KW - qualitative methods N2 - 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. UR - https://infodemiology.jmir.org/2022/2/e37134 UR - http://dx.doi.org/10.2196/37134 UR - http://www.ncbi.nlm.nih.gov/pubmed/35854815 ID - info:doi/10.2196/37134 ER - TY - JOUR AU - Kolluri, Nikhil AU - Liu, Yunong AU - Murthy, Dhiraj PY - 2022/8/25 TI - COVID-19 Misinformation Detection: Machine-Learned Solutions to the Infodemic JO - JMIR Infodemiology SP - e38756 VL - 2 IS - 2 KW - COVID-19 KW - misinformation KW - machine learning KW - fact-checking KW - infodemiology KW - infodemic management KW - model performance KW - model accuracy KW - content analysis N2 - 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. UR - https://infodemiology.jmir.org/2022/2/e38756 UR - http://dx.doi.org/10.2196/38756 UR - http://www.ncbi.nlm.nih.gov/pubmed/37113446 ID - info:doi/10.2196/38756 ER - TY - JOUR AU - Stevens, Hannah AU - Rasul, Ehab Muhammad AU - Oh, Jung Yoo PY - 2022/9/13 TI - Emotions and Incivility in Vaccine Mandate Discourse: Natural Language Processing Insights JO - JMIR Infodemiology SP - e37635 VL - 2 IS - 2 KW - vaccine hesitancy KW - COVID-19 KW - vaccine mandates KW - natural language processing KW - incivility KW - LIWC KW - Linguistic Inquiry and Word Count KW - Twitter N2 - 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. UR - https://infodemiology.jmir.org/2022/2/e37635 UR - http://dx.doi.org/10.2196/37635 UR - http://www.ncbi.nlm.nih.gov/pubmed/36188420 ID - info:doi/10.2196/37635 ER - TY - JOUR AU - Charbonneau, Esther AU - Mellouli, Sehl AU - Chouikh, Arbi AU - Couture, Laurie-Jane AU - Desroches, Sophie PY - 2022/9/16 TI - The Information Sharing Behaviors of Dietitians and Twitter Users in the Nutrition and COVID-19 Infodemic: Content Analysis Study of Tweets JO - JMIR Infodemiology SP - e38573 VL - 2 IS - 2 KW - nutrition KW - COVID-19 KW - dietitians KW - Twitter KW - public KW - themes KW - behavior KW - content accuracy KW - user engagement KW - content analysis KW - misinformation KW - disinformation KW - infodemic N2 - 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. UR - https://infodemiology.jmir.org/2022/2/e38573 UR - http://dx.doi.org/10.2196/38573 UR - http://www.ncbi.nlm.nih.gov/pubmed/36188421 ID - info:doi/10.2196/38573 ER - TY - JOUR AU - Zhan, Kevin AU - Li, Yutong AU - Osmani, Rafay AU - Wang, Xiaoyu AU - Cao, Bo PY - 2022/9/22 TI - Data Exploration and Classification of News Article Reliability: Deep Learning Study JO - JMIR Infodemiology SP - e38839 VL - 2 IS - 2 KW - COVID-19 KW - deep learning KW - news article reliability KW - false information KW - infodemic KW - ensemble model N2 - 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. UR - https://infodemiology.jmir.org/2022/2/e38839 UR - http://dx.doi.org/10.2196/38839 UR - http://www.ncbi.nlm.nih.gov/pubmed/36193330 ID - info:doi/10.2196/38839 ER - TY - JOUR AU - Abroms, C. Lorien AU - Yom-Tov, Elad PY - 2022/9/14 TI - The Role of Information Boxes in Search Engine Results for Symptom Searches: Analysis of Archival Data JO - JMIR Infodemiology SP - e37286 VL - 2 IS - 2 KW - health misinformation KW - search engine KW - internet search KW - information boxes KW - knowledge graph boxes KW - misinformation KW - health information KW - Microsoft KW - internet KW - data KW - symptoms KW - results KW - users KW - medical KW - Bing KW - USA KW - linear KW - logistic KW - regression KW - web KW - ads KW - behavior N2 - Background: Search engines provide health information boxes as part of search results to address information gaps and misinformation for commonly searched symptoms. Few prior studies have sought to understand how individuals who are seeking information about health symptoms navigate different types of page elements on search engine results pages, including health information boxes. Objective: Using real-world search engine data, this study sought to investigate how users searching for common health-related symptoms with Bing interacted with health information boxes (info boxes) and other page elements. Methods: A sample of searches (N=28,552 unique searches) was compiled for the 17 most common medical symptoms queried on Microsoft Bing by users in the United States between September and November 2019. The association between the page elements that users saw, their characteristics, and the time spent on elements or clicks was investigated using linear and logistic regression. Results: The number of searches ranged by symptom type from 55 searches for cramps to 7459 searches for anxiety. Users searching for common health-related symptoms saw pages with standard web results (n=24,034, 84%), itemized web results (n=23,354, 82%), ads (n=13,171, 46%), and info boxes (n=18,215, 64%). Users spent on average 22 (SD 26) seconds on the search engine results page. Users who saw all page elements spent 25% (7.1 s) of their time on the info box, 23% (6.1 s) on standard web results, 20% (5.7 s) on ads, and 10% (10 s) on itemized web results, with significantly more time on the info box compared to other elements and the least amount of time on itemized web results. Info box characteristics such as reading ease and appearance of related conditions were associated with longer time on the info box. Although none of the info box characteristics were associated with clicks on standard web results, info box characteristics such as reading ease and related searches were negatively correlated with clicks on ads. Conclusions: Info boxes were attended most by users compared with other page elements, and their characteristics may influence future web searching. Future studies are needed that further explore the utility of info boxes and their influence on real-world health-seeking behaviors. UR - https://infodemiology.jmir.org/2022/2/e37286 UR - http://dx.doi.org/10.2196/37286 UR - http://www.ncbi.nlm.nih.gov/pubmed/37113445 ID - info:doi/10.2196/37286 ER - TY - JOUR AU - Shan, Yi AU - Ji, Meng AU - Xie, Wenxiu AU - Zhang, Xiaomin AU - Ng Chok, Harrison AU - Li, Rongying AU - Qian, Xiaobo AU - Lam, Kam-Yiu AU - Chow, Chi-Yin AU - Hao, Tianyong PY - 2022/11/15 TI - COVID-19?Related Health Inequalities Induced by the Use of Social Media: Systematic Review JO - JMIR Infodemiology SP - e38453 VL - 2 IS - 2 KW - systematic review KW - social media use KW - health inequalities KW - COVID-19 KW - mobile phone N2 - Background: COVID-19?related health inequalities were reported in some studies, showing the failure in public health and communication. Studies investigating the contexts and causes of these inequalities pointed to the contribution of communication inequality or poor health literacy and information access to engagement with health care services. However, no study exclusively dealt with health inequalities induced by the use of social media during COVID-19. Objective: This review aimed to identify and summarize COVID-19?related health inequalities induced by the use of social media and the associated contributing factors and to characterize the relationship between the use of social media and health disparities during the COVID-19 pandemic. Methods: A systematic review was conducted on this topic in light of the protocol of the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 statement. Keyword searches were performed to collect papers relevant to this topic in multiple databases: PubMed (which includes MEDLINE [Ovid] and other subdatabases), ProQuest (which includes APA PsycINFO, Biological Science Collection, and others), ACM Digital Library, and Web of Science, without any year restriction. Of the 670 retrieved publications, 10 were initially selected based on the predefined selection criteria. These 10 articles were then subjected to quality analysis before being analyzed in the final synthesis and discussion. Results: Of the 10 articles, 1 was further removed for not meeting the quality assessment criteria. Finally, 9 articles were found to be eligible and selected for this review. We derived the characteristics of these studies in terms of publication years, journals, study locations, locations of study participants, study design, sample size, participant characteristics, and potential risk of bias, and the main results of these studies in terms of the types of social media, social media use?induced health inequalities, associated factors, and proposed resolutions. On the basis of the thematic synthesis of these extracted data, we derived 4 analytic themes, namely health information inaccessibility?induced health inequalities and proposed resolutions, misinformation-induced health inequalities and proposed resolutions, disproportionate attention to COVID-19 information and proposed resolutions, and higher odds of social media?induced psychological distress and proposed resolutions. Conclusions: This paper was the first systematic review on this topic. Our findings highlighted the great value of studying the COVID-19?related health knowledge gap, the digital technology?induced unequal distribution of health information, and the resulting health inequalities, thereby providing empirical evidence for understanding the relationship between social media use and health inequalities in the context of COVID-19 and suggesting practical solutions to such disparities. Researchers, social media, health practitioners, and policy makers can draw on these findings to promote health equality while minimizing social media use?induced health inequalities. UR - https://infodemiology.jmir.org/2022/2/e38453 UR - http://dx.doi.org/10.2196/38453 UR - http://www.ncbi.nlm.nih.gov/pubmed/36420437 ID - info:doi/10.2196/38453 ER - TY - JOUR AU - Weeks, Rose AU - White, Sydney AU - Hartner, Anna-Maria AU - Littlepage, Shea AU - Wolf, Jennifer AU - Masten, Kristin AU - Tingey, Lauren PY - 2022/11/25 TI - COVID-19 Messaging on Social Media for American Indian and Alaska Native Communities: Thematic Analysis of Audience Reach and Web Behavior JO - JMIR Infodemiology SP - e38441 VL - 2 IS - 2 KW - COVID-19 KW - American Indian or Alaska Native KW - social media KW - communication KW - tribal organization KW - community health KW - infodemiology KW - Twitter KW - online behavior KW - content analysis KW - thematic analysis N2 - 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. UR - https://infodemiology.jmir.org/2022/2/e38441 UR - http://dx.doi.org/10.2196/38441 UR - http://www.ncbi.nlm.nih.gov/pubmed/36471705 ID - info:doi/10.2196/38441 ER - TY - JOUR AU - Xu, Wayne Weiai AU - Tshimula, Marie Jean AU - Dubé, Ève AU - Graham, E. Janice AU - Greyson, Devon AU - MacDonald, E. Noni AU - Meyer, B. Samantha PY - 2022/12/9 TI - Unmasking the Twitter Discourses on Masks During the COVID-19 Pandemic: User Cluster?Based BERT Topic Modeling Approach JO - JMIR Infodemiology SP - e41198 VL - 2 IS - 2 KW - infoveillance KW - data analytics KW - Twitter KW - social media KW - user classification KW - COVID-19 N2 - Background: The COVID-19 pandemic has spotlighted the politicization of public health issues. A public health monitoring tool must be equipped to reveal a public health measure?s political context and guide better interventions. In its current form, infoveillance tends to neglect identity and interest-based users, hence being limited in exposing how public health discourse varies by different political groups. Adopting an algorithmic tool to classify users and their short social media texts might remedy that limitation. Objective: We aimed to implement a new computational framework to investigate discourses and temporal changes in topics unique to different user clusters. The framework was developed to contextualize how web-based public health discourse varies by identity and interest-based user clusters. We used masks and mask wearing during the early stage of the COVID-19 pandemic in the English-speaking world as a case study to illustrate the application of the framework. Methods: We first clustered Twitter users based on their identities and interests as expressed through Twitter bio pages. Exploratory text network analysis reveals salient political, social, and professional identities of various user clusters. It then uses BERT Topic modeling to identify topics by the user clusters. It reveals how web-based discourse has shifted over time and varied by 4 user clusters: conservative, progressive, general public, and public health professionals. Results: This study demonstrated the importance of a priori user classification and longitudinal topical trends in understanding the political context of web-based public health discourse. The framework reveals that the political groups and the general public focused on the science of mask wearing and the partisan politics of mask policies. A populist discourse that pits citizens against elites and institutions was identified in some tweets. Politicians (such as Donald Trump) and geopolitical tensions with China were found to drive the discourse. It also shows limited participation of public health professionals compared with other users. Conclusions: We conclude by discussing the importance of a priori user classification in analyzing web-based discourse and illustrating the fit of BERT Topic modeling in identifying contextualized topics in short social media texts. UR - https://infodemiology.jmir.org/2022/2/e41198 UR - http://dx.doi.org/10.2196/41198 UR - http://www.ncbi.nlm.nih.gov/pubmed/36536763 ID - info:doi/10.2196/41198 ER - TY - JOUR AU - DePaula, Nic AU - Hagen, Loni AU - Roytman, Stiven AU - Alnahass, Dana PY - 2022/12/20 TI - Platform Effects on Public Health Communication: A Comparative and National Study of Message Design and Audience Engagement Across Twitter and Facebook JO - JMIR Infodemiology SP - e40198 VL - 2 IS - 2 KW - platform effects KW - COVID-19 KW - social media KW - health communication KW - message design KW - risk communication KW - Twitter KW - Facebook KW - user engagement KW - e-government N2 - Background: Public health agencies widely adopt social media for health and risk communication. Moreover, different platforms have different affordances, which may impact the quality and nature of the messaging and how the public engages with the content. However, these platform effects are not often compared in studies of health and risk communication and not previously for the COVID-19 pandemic. Objective: This study measures the potential media effects of Twitter and Facebook on public health message design and engagement by comparing message elements and audience engagement in COVID-19?related posts by local, state, and federal public health agencies in the United States during the pandemic, to advance theories of public health messaging on social media and provide recommendations for tailored social media communication strategies. Methods: We retrieved all COVID-19?related posts from major US federal agencies related to health and infectious disease, all major state public health agencies, and selected local public health departments on Twitter and Facebook. A total of 100,785 posts related to COVID-19, from 179 different accounts of 96 agencies, were retrieved for the entire year of 2020. We adopted a framework of social media message elements to analyze the posts across Facebook and Twitter. For manual content analysis, we subsampled 1677 posts. We calculated the prevalence of various message elements across the platforms and assessed the statistical significance of differences. We also calculated and assessed the association between message elements with normalized measures of shares and likes for both Facebook and Twitter. Results: Distributions of message elements were largely similar across both sites. However, political figures (P<.001), experts (P=.01), and nonpolitical personalities (P=.01) were significantly more present on Facebook posts compared to Twitter. Infographics (P<.001), surveillance information (P<.001), and certain multimedia elements (eg, hyperlinks, P<.001) were more prevalent on Twitter. In general, Facebook posts received more (normalized) likes (0.19%) and (normalized) shares (0.22%) compared to Twitter likes (0.08%) and shares (0.05%). Elements with greater engagement on Facebook included expressives and collectives, whereas posts related to policy were more engaged with on Twitter. Science information (eg, scientific explanations) comprised 8.5% (73/851) of Facebook and 9.4% (78/826) of Twitter posts. Correctives of misinformation only appeared in 1.2% (11/851) of Facebook and 1.4% (12/826) of Twitter posts. Conclusions: In general, we find a data and policy orientation for Twitter messages and users and a local and personal orientation for Facebook, although also many similarities across platforms. Message elements that impact engagement are similar across platforms but with some notable distinctions. This study provides novel evidence for differences in COVID-19 public health messaging across social media sites, advancing knowledge of public health communication on social media and recommendations for health and risk communication strategies on these online platforms. UR - https://infodemiology.jmir.org/2022/2/e40198 UR - http://dx.doi.org/10.2196/40198 UR - http://www.ncbi.nlm.nih.gov/pubmed/36575712 ID - info:doi/10.2196/40198 ER - TY - JOUR AU - Al-Rawi, Ahmed AU - Fakida, Abdelrahman AU - Grounds, Kelly PY - 2022/7/26 TI - Investigation of COVID-19 Misinformation in Arabic on Twitter: Content Analysis JO - JMIR Infodemiology SP - e37007 VL - 2 IS - 2 KW - COVID-19 KW - Arab world KW - Twitter KW - misinformation KW - vaccination KW - infodemiology KW - vaccine hesitancy KW - infoveillance KW - health information KW - social media KW - social media content KW - content analysis KW - Twitter analysis N2 - Background: The COVID-19 pandemic has been occurring concurrently with an infodemic of misinformation about the virus. Spreading primarily on social media, there has been a significant academic effort to understand the English side of this infodemic. However, much less attention has been paid to the Arabic side. Objective: There is an urgent need to examine the scale of Arabic COVID-19 disinformation. This study empirically examines how Arabic speakers use specific hashtags on Twitter to express antivaccine and antipandemic views to uncover trends in their social media usage. By exploring this topic, we aim to fill a gap in the literature that can help understand conspiracies in Arabic around COVID-19. Methods: This study used content analysis to understand how 13 popular Arabic hashtags were used in antivaccine communities. We used Twitter Academic API v2 to search for the hashtags from the beginning of August 1, 2006, until October 10, 2021. After downloading a large data set from Twitter, we identified major categories or topics in the sample data set using emergent coding. Emergent coding was chosen because of its ability to inductively identify the themes that repeatedly emerged from the data set. Then, after revising the coding scheme, we coded the rest of the tweets and examined the results. In the second attempt and with a modified codebook, an acceptable intercoder agreement was reached (Krippendorff ??.774). Results: In total, we found 476,048 tweets, mostly posted in 2021. First, the topic of infringing on civil liberties (n=483, 41.1%) covers ways that governments have allegedly infringed on civil liberties during the pandemic and unfair restrictions that have been imposed on unvaccinated individuals. Users here focus on topics concerning their civil liberties and freedoms, claiming that governments violated such rights following the pandemic. Notably, users denounce government efforts to force them to take any of the COVID-19 vaccines for different reasons. This was followed by vaccine-related conspiracies (n=476, 40.5%), including a Deep State dictating pandemic policies, mistrusting vaccine efficacy, and discussing unproven treatments. Although users tweeted about a range of different conspiracy theories, mistrusting the vaccine?s efficacy, false or exaggerated claims about vaccine risks and vaccine-related diseases, and governments and pharmaceutical companies profiting from vaccines and intentionally risking the general public health appeared the most. Finally, calls for action (n=149, 12.6%) encourage individuals to participate in civil demonstrations. These calls range from protesting to encouraging other users to take action about the vaccine mandate. For each of these categories, we also attempted to trace the logic behind the different categories by exploring different types of conspiracy theories for each category. Conclusions: Based on our findings, we were able to identify 3 prominent topics that were prevalent amongst Arabic speakers on Twitter. These categories focused on violations of civil liberties by governments, conspiracy theories about the vaccines, and calls for action. Our findings also highlight the need for more research to better understand the impact of COVID-19 disinformation on the Arab world. UR - https://infodemiology.jmir.org/2022/2/e37007 UR - http://dx.doi.org/10.2196/37007 UR - http://www.ncbi.nlm.nih.gov/pubmed/35915823 ID - info:doi/10.2196/37007 ER - TY - JOUR AU - Yiannakoulias, Niko AU - Darlington, Connor J. AU - Slavik, E. Catherine AU - Benjamin, Grant PY - 2022/8/29 TI - Negative COVID-19 Vaccine Information on Twitter: Content Analysis JO - JMIR Infodemiology SP - e38485 VL - 2 IS - 2 KW - vaccine acceptance KW - vaccine hesitancy KW - Twitter KW - health communication KW - COVID-19 KW - social media KW - infodemiology KW - misinformation KW - content analysis KW - sentiment analysis KW - vaccine misinformation KW - web-based health information N2 - Background: Social media platforms, such as Facebook, Instagram, Twitter, and YouTube, have a role in spreading anti-vaccine opinion and misinformation. Vaccines have been an important component of managing the COVID-19 pandemic, so content that discourages vaccination is generally seen as a concern to public health. However, not all negative information about vaccines is explicitly anti-vaccine, and some of it may be an important part of open communication between public health experts and the community. Objective: This research aimed to determine the frequency of negative COVID-19 vaccine information on Twitter in the first 4 months of 2021. Methods: We manually coded 7306 tweets sampled from a large sampling frame of tweets related to COVID-19 and vaccination collected in early 2021. We also coded the geographic location and mentions of specific vaccine producers. We compared the prevalence of anti-vaccine and negative vaccine information over time by author type, geography (United States, United Kingdom, and Canada), and vaccine developer. Results: We found that 1.8% (131/7306) of tweets were anti-vaccine, but 21% (1533/7306) contained negative vaccine information. The media and government were common sources of negative vaccine information but not anti-vaccine content. Twitter users from the United States generated the plurality of negative vaccine information; however, Twitter users in the United Kingdom were more likely to generate negative vaccine information. Negative vaccine information related to the Oxford/AstraZeneca vaccine was the most common, particularly in March and April 2021. Conclusions: Overall, the volume of explicit anti-vaccine content on Twitter was small, but negative vaccine information was relatively common and authored by a breadth of Twitter users (including government, medical, and media sources). Negative vaccine information should be distinguished from anti-vaccine content, and its presence on social media could be promoted as evidence of an effective communication system that is honest about the potential negative effects of vaccines while promoting the overall health benefits. However, this content could still contribute to vaccine hesitancy if it is not properly contextualized. UR - https://infodemiology.jmir.org/2022/2/e38485 UR - http://dx.doi.org/10.2196/38485 UR - http://www.ncbi.nlm.nih.gov/pubmed/36348980 ID - info:doi/10.2196/38485 ER - TY - JOUR AU - Christensen, Bente AU - Laydon, Daniel AU - Chelkowski, Tadeusz AU - Jemielniak, Dariusz AU - Vollmer, Michaela AU - Bhatt, Samir AU - Krawczyk, Konrad PY - 2022/9/20 TI - Quantifying Changes in Vaccine Coverage in Mainstream Media as a Result of the COVID-19 Outbreak: Text Mining Study JO - JMIR Infodemiology SP - e35121 VL - 2 IS - 2 KW - data mining KW - COVID-19 KW - vaccine KW - text mining KW - change KW - coverage KW - communication KW - media KW - social media KW - news KW - outbreak KW - acceptance KW - hesitancy KW - understanding KW - knowledge KW - sentiment N2 - Background: Achieving herd immunity through vaccination depends upon the public?s acceptance, which in turn relies on their understanding of its risks and benefits. The fundamental objective of public health messaging on vaccines is therefore the clear communication of often complex information and, increasingly, the countering of misinformation. The primary outlet shaping public understanding is mainstream online news media, where coverage of COVID-19 vaccines was widespread. Objective: We used text-mining analysis on the front pages of mainstream online news to quantify the volume and sentiment polarization of vaccine coverage. Methods: We analyzed 28 million articles from 172 major news sources across 11 countries between July 2015 and April 2021. We employed keyword-based frequency analysis to estimate the proportion of overall articles devoted to vaccines. We performed topic detection using BERTopic and named entity recognition to identify the leading subjects and actors mentioned in the context of vaccines. We used the Vader Python module to perform sentiment polarization quantification of all collated English-language articles. Results: The proportion of front-page articles mentioning vaccines increased from 0.1% to 4% with the outbreak of COVID-19. The number of negatively polarized articles increased from 6698 in 2015-2019 to 28,552 in 2020-2021. However, overall vaccine coverage before the COVID-19 pandemic was slightly negatively polarized (57% negative), whereas coverage during the pandemic was positively polarized (38% negative). Conclusions: Throughout the pandemic, vaccines have risen from a marginal to a widely discussed topic on the front pages of major news outlets. Mainstream online media has been positively polarized toward vaccines, compared with mainly negative prepandemic vaccine news. However, the pandemic was accompanied by an order-of-magnitude increase in vaccine news that, due to low prepandemic frequency, may contribute to a perceived negative sentiment. These results highlight important interactions between the volume of news and overall polarization. To the best of our knowledge, our work is the first systematic text mining study of front-page vaccine news headlines in the context of COVID-19. UR - https://infodemiology.jmir.org/2022/2/e35121 UR - http://dx.doi.org/10.2196/35121 UR - http://www.ncbi.nlm.nih.gov/pubmed/36348981 ID - info:doi/10.2196/35121 ER - TY - JOUR AU - Luo, Kai AU - Yang, Yang AU - Teo, Hai Hock PY - 2022/12/8 TI - The Asymmetric Influence of Emotion in the Sharing of COVID-19 Science on Social Media: Observational Study JO - JMIR Infodemiology SP - e37331 VL - 2 IS - 2 KW - COVID-19 KW - science communication KW - emotion KW - COVID-19 science KW - online social networks KW - computational social science KW - social media N2 - Background: Unlike past pandemics, COVID-19 is different to the extent that there is an unprecedented surge in both peer-reviewed and preprint research publications, and important scientific conversations about it are rampant on online social networks, even among laypeople. Clearly, this new phenomenon of scientific discourse is not well understood in that we do not know the diffusion patterns of peer-reviewed publications vis-à-vis preprints and what makes them viral. Objective: This paper aimed to examine how the emotionality of messages about preprint and peer-reviewed publications shapes their diffusion through online social networks in order to inform health science communicators? and policy makers? decisions on how to promote reliable sharing of crucial pandemic science on social media. Methods: We collected a large sample of Twitter discussions of early (January to May 2020) COVID-19 medical research outputs, which were tracked by Altmetric, in both preprint servers and peer-reviewed journals, and conducted statistical analyses to examine emotional valence, specific emotions, and the role of scientists as content creators in influencing the retweet rate. Results: Our large-scale analyses (n=243,567) revealed that scientific publication tweets with positive emotions were transmitted faster than those with negative emotions, especially for messages about preprints. Our results also showed that scientists? participation in social media as content creators could accentuate the positive emotion effects on the sharing of peer-reviewed publications. Conclusions: Clear communication of critical science is crucial in the nascent stage of a pandemic. By revealing the emotional dynamics in the social media sharing of COVID-19 scientific outputs, our study offers scientists and policy makers an avenue to shape the discussion and diffusion of emerging scientific publications through manipulation of the emotionality of tweets. Scientists could use emotional language to promote the diffusion of more reliable peer-reviewed articles, while avoiding using too much positive emotional language in social media messages about preprints if they think that it is too early to widely communicate the preprint (not peer reviewed) data to the public. UR - https://infodemiology.jmir.org/2022/2/e37331 UR - http://dx.doi.org/10.2196/37331 UR - http://www.ncbi.nlm.nih.gov/pubmed/36536762 ID - info:doi/10.2196/37331 ER -