Social Interactions and Bias in the Evaluation of Online Reviews
Bibliographic record
Abstract
User-generated content, online reviews in particular, has been increasingly integrated into the management of customer responses and therefore into the core operations of platforms. Despite the extensive studies on the generation of online reviews and their potential impacts, research is scant regarding the factors that might affect the evaluation of online reviews by peer groups. In this study, we focus on the bias in review evaluations and argue that social interactions in various forms on online review platforms contribute to the bias. Using a unique dataset from a major review platform, we find that, ceteris paribus, reviews posted by more socially engaged users receive more helpfulness votes than those by less socially engaged users. Similarly, users tend to vote for reviews written by their mutual followers than for those written by non-followers. In addition, we find that less socially engaged users review a broader range of products (or services) but are less likely to stay on the platform, which may further contribute to the bias in review evaluations. Our findings, therefore, underscore an important factor—social factors—contributing to the bias in review evaluations and have implications for the management of customer response, content quality, and operational performance of platforms.
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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 | 0.090 | 0.361 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".