The Rhythm of The Algorithm: Behavioural Influences and TikTok Users
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
TikTok’s ubiquity, with over two billion downloads, has made the social media platform one of the most popular in the world. Such popularity necessitates information experts to be aware of the technological composition and effects that can be induced into user populations through algorithmic processes which modify and alter behaviour. The composition and purpose of algorithms are explored within a sociotechnical space. Correlations between algorithms, user activity, and user behaviour can be examined as a product of algorithmic influence. Algorithmic procedures have the potential to shape user behaviours, and as a consequence could shape future marketplaces. Within TikTok’s online spaces algorithms facilitate community formation. The literature suggests that algorithms are important in shaping digital community practices, with potential for spreading sociogenic illness. TikTok emphasizes the importance of studying online platforms regarding the spread of contagious behaviours and learning if social media plays a role in their development. Researchers lack consensus on how or whether behaviour modification is caused by algorithms through social media. The research indicates that companies aim to modify people’s decision-making processes due to these strategies having mass applicability in other contexts for the purpose of changing human behaviour.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.006 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it