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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2406.15991 |
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| _version_ | 1866909229993951232 |
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| author | Zhao, Xinyan Wong, Chau-Wai |
| author_facet | Zhao, Xinyan Wong, Chau-Wai |
| contents | Digital technologies and social algorithms are revolutionizing the media landscape, altering how we select and consume health information. Extending the selectivity paradigm with research on social media engagement, the convergence perspective, and algorithmic impact, this study investigates how individuals' liked TikTok videos on various health-risk topics are associated with their vaping and drinking behaviors. Methodologically, we relied on data linkage to objectively measure selective engagement on social media, which involves combining survey self-reports with digital traces from TikTok interactions for the consented respondents (n = 166). A computational analysis of 13,724 health-related videos liked by these respondents from 2020 to 2023 was conducted. Our findings indicate that users who initially liked drinking-related content on TikTok are inclined to favor more of such videos over time, with their likes on smoking, drinking, and fruit and vegetable videos influencing their self-reported vaping and drinking behaviors. Our study highlights the methodological value of combining digital traces, computational analysis, and self-reported data for a more objective examination of social media consumption and engagement, as well as a more ecologically valid understanding of social media's behavioral impact. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_15991 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | TikTok Engagement Traces Over Time and Health Risky Behaviors: Combining Data Linkage and Computational Methods Zhao, Xinyan Wong, Chau-Wai Computers and Society Human-Computer Interaction Digital technologies and social algorithms are revolutionizing the media landscape, altering how we select and consume health information. Extending the selectivity paradigm with research on social media engagement, the convergence perspective, and algorithmic impact, this study investigates how individuals' liked TikTok videos on various health-risk topics are associated with their vaping and drinking behaviors. Methodologically, we relied on data linkage to objectively measure selective engagement on social media, which involves combining survey self-reports with digital traces from TikTok interactions for the consented respondents (n = 166). A computational analysis of 13,724 health-related videos liked by these respondents from 2020 to 2023 was conducted. Our findings indicate that users who initially liked drinking-related content on TikTok are inclined to favor more of such videos over time, with their likes on smoking, drinking, and fruit and vegetable videos influencing their self-reported vaping and drinking behaviors. Our study highlights the methodological value of combining digital traces, computational analysis, and self-reported data for a more objective examination of social media consumption and engagement, as well as a more ecologically valid understanding of social media's behavioral impact. |
| title | TikTok Engagement Traces Over Time and Health Risky Behaviors: Combining Data Linkage and Computational Methods |
| topic | Computers and Society Human-Computer Interaction |
| url | https://arxiv.org/abs/2406.15991 |