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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.015 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.005 |
| 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".