Audience Participation in TikTok Metadata
Bibliographic record
Abstract
With the expansion of digital culture, an in-depth ref lection on how to research audiences is necessary. If, formerly, the individual was placed in a social category that defined cultural tastes, now technology identifies patterns of behavior from the direct record of their actions. This text explores the type of knowledge that can be obtained on audience participation on TikTok. We propose a methodology that consists of the analysis of usage metadata. The fieldwork focuses on “Ac2ality”, an information account with 4.4 million followers in Spain. We analysed all videos shared over six weeks of the first quarter of 2023 (n=173). The purpose was to find (a) the degree of the linear correlation between the metadata for the same video and (b) the existence of correlations between metadata and type of video/content. For each metadatum available with open access (comments, likes, saves, shares and views), four activity levels have been established (low, intermediate, high and very high). The majority trend indicates that the levels obtained by the metadata of the same content are not coincident, that is, a video will have more or less scope according to the observed metadata. The homogeneity of the videos means that only clear correlations between topic and metadata are detected. Topics with less presence can reach high levels of activity.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".