A summative content analysis: how is #Homelessness portrayed on TikTok?
Bibliographic record
Abstract
This study examines how homelessness is represented on TikTok. We performed a summative content analysis on the top 200 TikTok videos with the hashtag #homelessness on July 15, 2022. Four main content themes were developed to code the data: (1) content creators giving unhoused people money, food, services, shelter, and/or other forms of support; (2) content creators connecting with unhoused people; (3) content creators sharing their personal experiences with homelessness; and (4) portrayal of homelessness across all content. We found that content from our sample was rarely produced by those experiencing homelessness and mainly focused on those providing charitable donations rather than discussing sustainable solutions. Homelessness was mainly portrayed as affecting racialized adult men living on the street, which may further perpetuate existing stereotypes associated with homelessness. Future research exploring content exclusively produced by unhoused individuals is warranted, including those from teenagers, young adults, women, and gender diverse individuals, to understand and improve their representation in public discourse. In order to advocate for policy changes that address the systemic factors contributing to homelessness, further efforts are needed to increase public awareness regarding the demographics of those experiencing homelessness, the underlying causes of this issue, and potential solutions.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".