Impacts of Social Interactions and Peer Evaluations on Online Review Platforms
Bibliographic record
Abstract
Social technologies on online review platforms enable social interactions among users, such as establishing following relationships and commenting on others’ posts. Although it is well recognized that more socially engaged reviewers tend to be more active and generate content of higher quality, our knowledge about the impact of social interactions on peer evaluations of reviews is limited. To address this issue, we use a unique dataset from a major online review platform and find that, ceteris paribus, reviews posted by more socially engaged users receive more helpfulness votes than those posted by less socially engaged users. Similarly, users tend to vote more for reviews written by their mutual followers than for those written by nonfollowers. In addition, we find that less socially engaged users review a broader range of products and services but are less likely to stay on a platform, which may further contribute to the inflation of peer evaluations (toward online reviews). Our study provides unique empirical evidence regarding the influence of social interactions on review evaluations. Furthermore, we caution researchers and practitioners against utilizing review helpfulness scores as a sole measure for review quality and diagnosticity.
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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 | 0.020 | 0.197 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".