VR content and its prosocial impact: predictors, moderators, and mediators of media effects. A systematic literature review
Bibliographic record
Abstract
The main purpose of this paper is to explore the prosocial impact of virtual reality (VR) audiovisual content based on a systematic literature review of empirical research on immersive VR media's potential to elicit prosocial behaviors. The illusion of place, verisimilitude, and virtual corporeality are the main elements that underpin the creation of immersive experiences that can turn the user into an active subject of the narrative, engaging with the audiovisual content and feeling the emotions it elicits. A virtual reality system that can offer these three elements provides the means to transform not only the user's sensation of space and reality, but even the users themselves. The question this paper seeks to answer is whether audiovisual VR content can influence an individual's thoughts and feelings about otherness, thereby eliciting prosocial behaviors rooted in a sense of social justice, equality and fairness. To this end, it presents a systematic literature review in accordance with the guidelines of the PRISMA statement, applying a self-deductive coding system based on the Differential Susceptibility to Media Effects Model. The review identifies trends in research on the prosocial potential of VR content, among which perspective taking stands out as one of the most common strategies. In addition, predictors, moderators, mediators, effects, and their correlations are identified in the research reviewed.
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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 | 0.016 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".