Effects of time-based Biases in Review Communities
نویسندگان
چکیده
[0.0] Abstract Prior work has used social biases to inform predictive models of evaluation-driven networks, but there still exist many opportunities to analyze and expand on frameworks for modeling the growth of review-based communities. In particular, we would like to more deeply explore the influence of the time of a review’s creation and the effect it has on other review metrics. In an observational study, we analyzed an Amazon Product Review data set to see if earlier reviews influenced successive, a behavior consistent with that of the Anchoring effect from work in social psychology. Our core analysis found that when there are a low number of reviews (~ 15), earlier reviews have a tendency of being closer to the mean than later reviews while this implies that there may indeed exist an anchoring effect, further analysis found that for the same fixed bounds in review number, earlier reviews also were generally deemed more helpful. Through a comparative study with the similarly 5-star scaled Beer Advocate beverage evaluation network review data set, we conclude that certain divergent trends found in the Amazon Review dataset can be attributed to its review helpfulness mechanism, rather than an inherent anchoring effect based simply on time.
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