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Synthetic Cannabinoids in Prisons: Content Analysis of TikToks
JMIR Infodemiology 2022;2(1):e37632
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| 5936 | 9 | 3 | |
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TikTok as a Source of Health Information and Misinformation for Young Women in the United States: Survey Study
JMIR Infodemiology 2024;4(1):e54663
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| 3700 | 6 | 21 | |
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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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| 3617 | 24 | 6 | |
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Corpus-Based Discourse Analysis of a Reddit Community of Users of Crystal Methamphetamine: Mixed Methods Study
JMIR Infodemiology 2023;3(1):e48189
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| 2952 | 12 | 0 | |
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Exploring How Youth Use TikTok for Mental Health Information in British Columbia: Semistructured Interview Study With Youth
JMIR Infodemiology 2024;4(1):e53233
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| 1889 | 47 | 8 | |
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Perceptions of Health Misinformation on Social Media: Cross-Sectional Survey Study
JMIR Infodemiology 2024;4(1):e51127
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| 1517 | 9 | 7 | |
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The Role of Social Media in Health Misinformation and Disinformation During the COVID-19 Pandemic: Bibliometric Analysis
JMIR Infodemiology 2023;3(1):e48620
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| 1335 | 7 | 18 | |
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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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| 1191 | 153 | 28 | |
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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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| 1156 | 271 | 92 | |
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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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| 945 | 27 | 11 | |
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Ethical Considerations in Infodemic Management: Systematic Scoping Review
JMIR Infodemiology 2024;4(1):e56307
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| 769 | 8 | 6 | |
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Evaluating the Influence of Role-Playing Prompts on ChatGPT’s Misinformation Detection Accuracy: Quantitative Study
JMIR Infodemiology 2024;4(1):e60678
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| 742 | 0 | 1 | |
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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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| 734 | 116 | 55 | |
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Reproductive Health Experiences Shared on TikTok by Young People: Content Analysis
JMIR Infodemiology 2023;3(1):e42810
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| 723 | 6 | 5 | |
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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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| 674 | 17 | 68 | |
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Public Figure Vaccination Rhetoric and Vaccine Hesitancy: Retrospective Twitter Analysis
JMIR Infodemiology 2023;3(1):e40575
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| 633 | 7 | 6 | |
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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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| 597 | 28 | 11 | |
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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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| 595 | 157 | 7 | |
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Understanding and Combating Misinformation: An Evolutionary Perspective
JMIR Infodemiology 2024;4(1):e65521
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| 590 | 4 | 1 | |
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Large Language Models Can Enable Inductive Thematic Analysis of a Social Media Corpus in a Single Prompt: Human Validation Study
JMIR Infodemiology 2024;4(1):e59641
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| 578 | 2 | 3 | |
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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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| 543 | 76 | 31 | |
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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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| 514 | 8 | 11 | |
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The Early Detection of Fraudulent COVID-19 Products From Twitter Chatter: Data Set and Baseline Approach Using Anomaly Detection
JMIR Infodemiology 2023;3(1):e43694
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| 513 | 3 | 0 | |
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Using Machine Learning Technology (Early Artificial Intelligence–Supported Response With Social Listening Platform) to Enhance Digital Social Understanding for the COVID-19 Infodemic: Development and Implementation Study
JMIR Infodemiology 2023;3(1):e47317
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| 513 | 3 | 6 | |
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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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| 501 | 14 | 4 | |