TikTok as information space: A scoping review of information behavior on TikTok
Bibliographic record
Abstract
TikTok, one of the fastest growing social networking apps globally, has generated a lot of scholarly and media attention in recent years. To surface the extent to which information behavior (IB) has been investigated on TikTok, a scoping review of 49 journal and conference papers was conducted to examine characteristics of the literature and coverage of IB phenomena. Papers related to TikTok and IB increased between 2020 and 2024, but publications in LIS venues were limited. The majority of authors were United States based, which may have implications for research generalizability. The surveyed papers featured a variety of methodologies, namely user interviews and surveys, and content analysis of videos. Use of LIS models, theories, and concepts was limited; while this reflects the multidisciplinary nature of TikTok research, it also meant that aspects of IB, such as re-finding, avoidance, and discovery, were underexplored and undertheorized. TikTok's algorithmic recommendation system and design features influenced information seeking and retrieval, discovery, evaluation, and sharing on the platform, but more empirical studies are needed to understand TikTok's role as an information space and its integration in the broader information behavior ecosystem. • This scoping review examined 49 information behavior-related papers featuring TikTok. • Scholarly interest in information aspects of TikTok increased from 2020 to 2024. • Papers featured multiple conceptual approaches; LIS specific frameworks were lacking. • Algorithmic engagement is shaping information seeking, discovery, sharing and evaluation. • Re -finding and avoidance information behaviors emerged as areas for future research.
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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 | 0.013 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.019 | 0.020 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".