Feeling lonely in the online crowd: what TikTok tells us about young people and loneliness
Bibliographic record
Abstract
Loneliness is a pressing public health issue, with young people particularly affected. Given the widespread use of TikTok among younger demographics, the platform plays an influential role in shaping perceptions of health topics. This study examines the self-perceived causes and experiences of loneliness expressed by TikTok users. We built a dataset of the most popular, publicly available English-language TikTok videos globally that used the hashtag #lonely and performed a qualitative content analysis to assess (i) video tone, (ii) self-expressed identities related to loneliness, (iii) unmet desires expressed as the cause of loneliness, (iii) absences expressed as the cause of loneliness, and (v) changes in life circumstance as the cause of loneliness. The included videos (n = 184) generated over 687 million views. Absence (69.6%) was the prevailing theme of self-perceived causes of loneliness. This absence was mostly attributed to a lack of quality in a person's relationships (33.7%) or having no relationship (32.1%). Mental health (26.1%) was the most cited expression of identity related to the experience of loneliness. The overall sentiment of the videos was negative (83.2%). No videos were made by healthcare professionals or healthcare organizations offering health information or support. TikTok is an accessible, underutilized, and valuable data resource to understand the public portrayal and sentiment of pressing health topics in young people. Results reveal a prevalence of negatively toned videos, primarily expressing absence as a root cause of loneliness.
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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.000 | 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".