Seeking Mental Health Support Among College Students in Video-Based Social Media: Content and Statistical Analysis of YouTube Videos
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: Mental health is a highly stigmatized disease, especially for young people. Due to its free, accessible format, college students increasingly use video-based social media for many aspects of information needs, including how-to tips, career, or health-related needs. The accessibility of video-based social media brings potential in supporting stigmatized contexts, such as college students' mental health. Understanding which kinds of videos about college students' mental health have increased viewer engagement will help build a foundation for exploring this potential. Little research has been done to identify video types systematically, how they have changed over time, and their associations on viewer engagement both short term and long term. OBJECTIVE: This study aims to identify strategies for using video-based social media to combat stigmatized diseases, such as mental health, among college students. We identify who, with what perspective, purpose, and content, makes up the videos available on social media (ie, YouTube) about college students' mental health and how these factors associate with viewer engagement. We then identify effective strategies for designing video-based social media content for supporting college students' mental health. METHODS: We performed inductive content analysis to identify different types of YouTube videos concerning college students' mental health (N=452) according to video attributes, including poster, perspective, and purpose. Time analysis showed how video types have changed over time. Fisher's exact test was used to examine the relationships between video attributes. The Mann-Whitney U test was used to test the association between video types and viewer engagement. Lastly, we investigated the difference in viewer engagement across time between two major types of videos (ie, individuals' storytelling and organization's informational videos). RESULTS: Time trend analysis showed a notable increase in the number of (1) videos by individuals, (2) videos that represent students' perspectives, and (3) videos that share stories and experiential knowledge over the recent years. Fisher's exact test found all video attributes (ie, poster, perspective, and purpose) are significantly correlated with each other. In addition, the Mann-Whitney U test found that poster (individual vs organization) and purpose (storytelling vs sharing information) type has a significant association with viewer engagement (P<.001). Lastly, individuals' storytelling videos had a greater engagement in the short term and the long term. CONCLUSIONS: The study shows that YouTube videos on college students' mental health can be well differentiated by the types of posters and the purpose of the videos. Taken together, the videos where individuals share their personal stories, as well as experiential knowledge (ie, tips and advice), engaged more viewers in both the short term and long term. Individuals' videos on YouTube showed the potential to support college students' mental health in unique ways, such as providing social support, validating experience, and sharing the positive experience of help-seeking.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it