Intentional news avoidance on short-form video platforms: a moderated mediation model of psychological reactance and relative entertainment motivation
Bibliographic record
Abstract
Abstract Previous studies have identified a correlation between individuals’ perception of news quality and their intention to avoid political news. However, limited research exists that examines the mechanisms that mediate or moderate this relationship, particularly in the context of short-form video platforms. This study, using a sample of 523 active users of Douyin, a Chinese short-form video platform, addresses this gap by examining the mediating influence of psychological reactance and the moderating impact of relative entertainment motivation on the connection between perceived news quality and intentional news avoidance. The study finds that the negative association between perceived news quality and intentional news avoidance is mediated by psychological reactance, while both the direct effect of perceived news quality on intentional news avoidance, and the indirect effect through psychological reactance, are moderated by relative entertainment motivation. Specifically, these effects are stronger for individuals with high relative entertainment motivation. These findings suggest that if users possess high relative entertainment motivation, their psychological reactance and intentional news avoidance can be reduced if the quality of news improves. This study contributes to current understanding of users’ deliberate avoidance of news and offers insights for owners and developers of short-form video platforms and algorithms.
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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 | 0.002 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| 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 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".