Personalized Recommendation Algorithms on Short Video Platforms: User Experience, Ethical Concerns, and Social Impact
Bibliographic record
Abstract
This paper discusses how algorithmic personalisation is being utilised among short video platforms, with an emphasis on Douyin in particular. In this paper, I integrate an in-depth literature review and original survey evidence to explore how personalised recommendation algorithms affect user engagement, content exposure, and perceptions of fairness and privacy. The results indicate that such algorithms significantly increase user screen time and influence opinion formation, while also leading to repeated exposure to similar content, the emergence of filter bubbles, and unequal visibility for less popular creators. Although many individuals report high awareness of how these systems work, their ability to meaningfully control or adjust algorithmic outputs remains limited, and concerns around data privacy are widespread. This paper highlights a central irony: while algorithmic media enhance satisfaction and platform retention, they simultaneously pose serious ethical challenges. This work concludes by emphasising the need for greater algorithmic transparency, stronger user agency, and fairness-focused design to ensure more accountable and inclusive digital media environments.
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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 | 0.006 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".