Affect and prediction in short-video social media recommendation algorithm: TikTok and the missing half-second
Bibliographic record
Abstract
This article examines the relationship between prediction and serendipity in the short-video social media platform TikTok, analyzing its recommendation algorithm through the lenses of affective and pragmatic turns in cognitive science. By looking at TikTok’s user experience, I demonstrate that while predictive models are crucial for user engagement, elements of surprise and unpredictability are equally essential for maintaining user interest. The study draws on theories of perception, emotion processing, and affect to provide a comprehensive understanding of the cognitive and embodied dimensions of digital social media experiences. I argue that TikTok’s success lies in its unique integration of both predictive accuracy and serendipitous discovery, creating an “indeterminacy center” that keeps users engaged. This research contributes to the broader understanding of social media dynamics, offering insights into the balance between prediction and serendipity in digital platforms.
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".