Impact of Social Media Usage on Users’ COVID-19 Protective Behavior: Survey Study in Indonesia
Bibliographic record
Abstract
BACKGROUND: Social media have become the source of choice for many users to search for health information on COVID-19 despite possible detrimental consequences. Several studies have analyzed the association between health information-searching behavior and mental health. Some of these studies examined users' intentions in searching health information on social media and the impact of social media use on mental health in Indonesia. OBJECTIVE: This study investigates both active and passive participation in social media, shedding light on cofounding effects from these different forms of engagement. In addition, this study analyses the role of trust in social media platforms and its effect on public health outcomes. Thus, the purpose of this study is to analyze the impact of social media usage on COVID-19 protective behavior in Indonesia. The most commonly used social media platforms are Instagram, Facebook, YouTube, TikTok, and Twitter. METHODS: We used primary data from an online survey. We processed 414 answers to a structured questionnaire to evaluate the relationship between these users' active and passive participation in social media, trust in social media, anxiety, self-efficacy, and protective behavior to COVID-19. We modeled the data using partial least square structural equation modeling. RESULTS: This study reveals that social media trust is a crucial antecedent, where trust in social media is positively associated with active contribution and passive consumption of COVID-19 content in social media, users' anxiety, self-efficacy, and protective behavior. This study found that active contribution of content related to COVID-19 on social media is positively correlated with anxiety, while passive participation increases self-efficacy and, in turn, protective behavior. This study also found that active participation is associated with negative health outcomes, while passive participation has the opposite effects. The results of this study can potentially be used for other infectious diseases, for example, dengue fever and diseases that can be transmitted through the air and have handling protocols similar to that of COVID-19. CONCLUSIONS: Public health campaigns can use social media for health promotion. Public health campaigns should post positive messages and distil the received information parsimoniously to avoid unnecessary and possibly counterproductive increased anxiety of the users.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".