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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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| 4 | 76 | 1 | |
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Change in Threads on Twitter Regarding Influenza, Vaccines, and Vaccination During the COVID-19 Pandemic: Artificial Intelligence–Based Infodemiology Study
JMIR Infodemiology 2021;1(1):e31983
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| 2 | 40 | 0 | |
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The Impact of the Online COVID-19 Infodemic on French Red Cross Actors’ Field Engagement and Protective Behaviors: Mixed Methods Study
JMIR Infodemiology 2021;1(1):e27472
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| 2 | 17 | 0 | |
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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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| 0 | 6 | 0 | |
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COVID-19–Associated Misinformation Across the South Asian Diaspora: Qualitative Study of WhatsApp Messages
JMIR Infodemiology 2023;3:e38607
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| 0 | 19 | 0 | |
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Users’ Modifications to Electronic Nicotine Delivery Systems: Content Analysis of YouTube Video Comments
JMIR Infodemiology 2022;2(2):e38268
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| 0 | 11 | 0 | |
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Media Data and Vaccine Hesitancy: Scoping Review
JMIR Infodemiology 2022;2(2):e37300
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| 0 | 4 | 0 | |
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Codeveloping and Evaluating a Campaign to Reduce Dementia Misconceptions on Twitter: Machine Learning Study
JMIR Infodemiology 2022;2(2):e36871
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| 0 | 13 | 0 | |
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Skin Cancer Narratives on Instagram: Content Analysis
JMIR Infodemiology 2022;2(1):e34940
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| 0 | 11 | 0 | |
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Health Care Providers’ Trusted Sources for Information About COVID-19 Vaccines: Mixed Methods Study
JMIR Infodemiology 2021;1(1):e33330
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| 0 | 11 | 0 | |
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Partisan Differences in Legislators’ Discussion of Vaccination on Twitter During the COVID-19 Era: Natural Language Processing Analysis
JMIR Infodemiology 2022;2(1):e32372
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| 0 | 29 | 0 | |
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Sustained Reductions in Online Search Interest for Communicable Eye and Other Conditions During the COVID-19 Pandemic: Infodemiology Study
JMIR Infodemiology 2022;2(1):e31732
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| 0 | 5 | 0 | |
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The Impact of COVID-19 on Conspiracy Hypotheses and Risk Perception in Italy: Infodemiological Survey Study Using Google Trends
JMIR Infodemiology 2021;1(1):e29929
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| 0 | 19 | 0 | |
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Public Attitudes and Factors of COVID-19 Testing Hesitancy in the United Kingdom and China: Comparative Infodemiology Study
JMIR Infodemiology 2021;1(1):e26895
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| 0 | 9 | 0 | |
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Platform Effects on Public Health Communication: A Comparative and National Study of Message Design and Audience Engagement Across Twitter and Facebook
JMIR Infodemiology 2022;2(2):e40198
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| 0 | 10 | 0 | |
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The Information Sharing Behaviors of Dietitians and Twitter Users in the Nutrition and COVID-19 Infodemic: Content Analysis Study of Tweets
JMIR Infodemiology 2022;2(2):e38573
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| 0 | 3 | 0 | |
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Web-Based Perspectives of Deemed Consent Organ Donation Legislation in Nova Scotia: Thematic Analysis of Commentary in Facebook Groups
JMIR Infodemiology 2022;2(2):e38242
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| 0 | 4 | 0 | |
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The Role of Information Boxes in Search Engine Results for Symptom Searches: Analysis of Archival Data
JMIR Infodemiology 2022;2(2):e37286
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| 0 | 4 | 0 | |
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COVID-19 and Tweets About Quitting Cigarette Smoking: Topic Model Analysis of Twitter Posts 2018-2020
JMIR Infodemiology 2022;2(1):e36215
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| 0 | 16 | 0 | |
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Factors Affecting Physicians’ Credibility on Twitter When Sharing Health Information: Online Experimental Study
JMIR Infodemiology 2022;2(1):e34525
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| 0 | 8 | 0 | |
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Exploring Google Searches for Out-of-Clinic Medication Abortion in the United States During 2020: Infodemiology Approach Using Multiple Samples
JMIR Infodemiology 2022;2(1):e33184
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| 0 | 11 | 0 | |
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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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| 0 | 16 | 0 | |
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Temporal Variations and Spatial Disparities in Public Sentiment Toward COVID-19 and Preventive Practices in the United States: Infodemiology Study of Tweets
JMIR Infodemiology 2021;1(1):e31671
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| 0 | 5 | 0 | |
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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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| 0 | 15 | 0 | |
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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:e40156
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| 0 | 5 | 0 | |