Dissemination of Information on Selective Serotonin Reuptake Inhibitors on TikTok: Analytical Mixed Methods Study of Creator Types, Content Tone, and User Engagement
Bibliographic record
Abstract
Background: TikTok [ByteDance] is a significant source of mental health-related content, including discussions on selective serotonin reuptake inhibitors (SSRIs). While the app fosters community building, its algorithm also amplifies misinformation as influencers without relevant expertise often dominate conversations about SSRIs. These videos frequently highlight personal experiences, potentially overshadowing evidence-based information from health care professionals. Despite these concerns, TikTok holds potential as a tool for improving mental health literacy when used by professionals to provide credible information. Objective: This study aimed to examine TikTok videos on SSRIs, hypothesizing that content will predominantly emphasize negative experiences and that videos by nonmedical professionals will attract higher engagement. By analyzing creators, engagement metrics, content tone, and video tone, this study aimed to shed light on social media's role in shaping perceptions of SSRIs and mental health literacy. Methods: A sample of 99 TikTok videos was collected on December 8, 2024. Apify, a web scraper, compiled pertinent engagement metrics (URLs, likes, comments, and shares). Views were manually recorded. In total, 3 researchers evaluated video and content tones and documented findings in Qualtrics. User profiles were analyzed to classify creators as a "medical professional" or "nonmedical professional" based on verification of their credentials. Statistical analyses evaluated the hypotheses. Results: The number of videos created by both nonmedical and medical professionals was roughly even. Approximately one-third (35/99, 35%) mentioned a specific SSRI (ie, fluoxetine, fluvoxamine, vilazodone, sertraline, paroxetine, citalopram, or escitalopram). Compared to medical professionals, nonmedical creators produced significantly more videos with a positive video tone (P<.001). TikToks made by both groups of creators, however, had negative content tones (P=.78). Nonmedical professionals received significantly greater overall views (P=.01), likes (P=.01), and comments (P=.03), but overall shares were not significantly different (P=.18). Daily interaction metrics revealed that nonmedical professionals received more daily interaction, but these differences were not significant in terms of views (P=.09), likes (P=.06), comments (P=.15), or shares (P=.28). Conclusions: Results showed that while both creator groups focused on negative SSRI side effects and experiences (content tone), the way they presented this information (video tone) differed. Medical professionals generally maintained a neutral video tone, whereas nonmedical professionals were more likely to adopt a positive video tone. This may explain why nonmedical professionals' videos had significantly more cumulative views, likes, and comments than medical professionals' videos. These findings are consistent with other research suggesting that the TikTok algorithm and users are more likely to favor and engage with videos that evoke a strong emotional response and are perceived as relatable to viewers. This study highlights the need for medical professionals to improve their approach to content creation on TikTok by using a more positive video tone to increase engagement.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".