Features of YouTube <sup>™</sup> videos produced by individuals who self-identify with borderline personality disorder
Bibliographic record
Abstract
OBJECTIVES: Many individuals use YouTube™ to seek out information and share first-hand experiences about mental illnesses, as well as to gain a sense of community. YouTube™ use may be especially appealing when offline supports are lacking or difficult to access, and when there is a fear of stigmatisation. Borderline personality disorder (BPD), also referred to as emotionally unstable personality disorder (EUPD), is a complex and often stigmatised mental-health disorder. The primary objective of this study was to describe the dominant messages that individuals who self-identify with the diagnosis of BPD present through YouTube™ videos. METHODS: The content analysis method was used to review 349 first-person YouTube™ uploads. Videos were coded for information regarding video and vlogger characteristics, video type, vlogger motivation and video content. Associations between video features including upload date and style and vlogger experience and motivation were examined. RESULTS: Findings indicate that more people who self-identify as being diagnosed with BPD are creating YouTube™ videos about their experiences, and these videos have shifted over time from being mostly anonymous multimedia productions to being monologues where the vlogger speaks directly to their audience. Discussions related to DSM-5 symptoms, treatment, effective coping and hope for the future are elements found in the uploads. CONCLUSION: The nature and content of BPD first-person YouTube™ uploads has increased and changed over time. Increased awareness of these changes may assist mental-health practitioners to support clients and direct them to explore uploads that offer hope and promote engagement in help-seeking and effective coping behaviours.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".