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Synthetic Cannabinoids in Prisons: Content Analysis of TikToks
JMIR Infodemiology 2022;2(1):e37632
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| 628 | 9 | 0 | |
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Establishing Infodemic Management in Germany: A Framework for Social Listening and Integrated Analysis to Report Infodemic Insights at the National Public Health Institute
JMIR Infodemiology 2023;3(1):e43646
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| 268 | 24 | 1 | |
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Advertising Alternative Cancer Treatments and Approaches on Meta Social Media Platforms: Content Analysis
JMIR Infodemiology 2023;3(1):e43548
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| 251 | 143 | 0 | |
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Measuring the Burden of Infodemics: Summary of the Methods and Results of the Fifth WHO Infodemic Management Conference
JMIR Infodemiology 2023;3(1):e44207
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| 247 | 153 | 5 | |
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Infodemic Signal Detection During the COVID-19 Pandemic: Development of a Methodology for Identifying Potential Information Voids in Online Conversations
JMIR Infodemiology 2021;1(1):e30971
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| 221 | 116 | 31 | |
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Global Misinformation Spillovers in the Vaccination Debate Before and During the COVID-19 Pandemic: Multilingual Twitter Study
JMIR Infodemiology 2023;3(1):e44714
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| 207 | 24 | 0 | |
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Media Data and Vaccine Hesitancy: Scoping Review
JMIR Infodemiology 2022;2(2):e37300
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| 170 | 4 | 2 | |
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A Public Health Research Agenda for Managing Infodemics: Methods and Results of the First WHO Infodemiology Conference
JMIR Infodemiology 2021;1(1):e30979
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| 157 | 271 | 57 | |
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Exploring Chronic Pain and Pain Management Perspectives: Qualitative Pilot Analysis of Web-Based Health Community Posts
JMIR Infodemiology 2023;3(1):e41672
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| 112 | 2 | 0 | |
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Characterizing the Discourse of Popular Diets to Describe Information Dispersal and Identify Leading Voices, Interaction, and Themes of Mental Health: Social Network Analysis
JMIR Infodemiology 2023;3(1):e38245
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| 107 | 14 | 0 | |
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Impact of the World Inflammatory Bowel Disease Day and Crohn’s and Colitis Awareness Week on Population Interest Between 2016 and 2020: Google Trends Analysis
JMIR Infodemiology 2021;1(1):e32856
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| 105 | 4 | 9 | |
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Desensitization to Fear-Inducing COVID-19 Health News on Twitter: Observational Study
JMIR Infodemiology 2021;1(1):e26876
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| 102 | 76 | 21 | |
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Monitoring Depression Trends on Twitter During the COVID-19 Pandemic: Observational Study
JMIR Infodemiology 2021;1(1):e26769
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| 95 | 17 | 41 | |
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Content Quality of YouTube Videos About Pain Management After Cesarean Birth: Content Analysis
JMIR Infodemiology 2023;3(1):e40802
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| 80 | 3 | 0 | |
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Open-Source Intelligence for Detection of Radiological Events and Syndromes Following the Invasion of Ukraine in 2022: Observational Study
JMIR Infodemiology 2023;3(1):e39895
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| 71 | 33 | 0 | |
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Examining Public Sentiments and Attitudes Toward COVID-19 Vaccination: Infoveillance Study Using Twitter Posts
JMIR Infodemiology 2022;2(1):e33909
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| 63 | 5 | 15 | |
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Skin Cancer Narratives on Instagram: Content Analysis
JMIR Infodemiology 2022;2(1):e34940
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| 61 | 11 | 4 | |
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Misinformation About and Interest in Chlorine Dioxide During the COVID-19 Pandemic in Mexico Identified Using Google Trends Data: Infodemiology Study
JMIR Infodemiology 2022;2(1):e29894
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| 61 | 15 | 5 | |
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Identifying Frames of the COVID-19 Infodemic: Thematic Analysis of Misinformation Stories Across Media
JMIR Infodemiology 2022;2(1):e33827
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| 59 | 8 | 7 | |
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Lessons Learned From Interdisciplinary Efforts to Combat COVID-19 Misinformation: Development of Agile Integrative Methods From Behavioral Science, Data Science, and Implementation Science
JMIR Infodemiology 2023;3(1):e40156
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| 52 | 5 | 1 | |
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Advancing Infodemiology in a Digital Intensive Era
JMIR Infodemiology 2022;2(1):e37115
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| 51 | 32 | 8 | |
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Public Opinion and Sentiment Before and at the Beginning of COVID-19 Vaccinations in Japan: Twitter Analysis
JMIR Infodemiology 2022;2(1):e32335
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| 51 | 16 | 11 | |
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Investigating COVID-19 Vaccine Communication and Misinformation on TikTok: Cross-sectional Study
JMIR Infodemiology 2022;2(2):e38316
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| 50 | 28 | 6 | |
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Unmasking the Twitter Discourses on Masks During the COVID-19 Pandemic: User Cluster–Based BERT Topic Modeling Approach
JMIR Infodemiology 2022;2(2):e41198
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| 48 | 6 | 2 | |
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The Quality, Readability, and Accuracy of the Information on Google About Cannabis and Driving: Quantitative Content Analysis
JMIR Infodemiology 2023;3(1):e43001
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| 48 | 3 | 0 | |