[4O3-GS-4] Web intelligence: behaviour analysis

[4O3-GS-4-03] Online reviews with heretical opinions offer helpful information: the analyzes of the mechanism of online collective intelligence

〇Kunhao Yang¹, Kazuhiro Ueda² (1. Waseda University, 2. The University of Tokyo)

[[Online]]

Keywords:Web mining, Crowdsourcing, Collective intelligence, Peer pressure

How to identify helpful information from large-scale online reviews has become a prominent issue in studies of wisdom-of-crowds. In this research, we focused on the online reviews with heretical opinions (i.e., heretical reviews). We examined whether these reviews could provide more helpful information compared to reviews with orthodox opinions. Using sentiment analysis, sentence-embedding methods, and simulations based on Bayesian inference for a large-scale dataset, we found that heretical reviews were deemed more helpful because they provided more sufficient, neutral, and unique information. To interpret these results, we considered that the reviewers of heretical reviews could face peer pressure when expressing heretical opinions. This peer pressure motivated the reviewers of heretical reviews to offer more convincing evidence (i.e., sufficient, neutral, and unique information) to persuade their readers. This study offers a simple, but effective approach to elicit helpful information from a large-scale of online reviews. Additionally, this study also provided a deeper understanding of the mechanism underlying online review behaviors: previous studies always considered that peer pressure causes biases in collective online behavior; however, this study uncovered that peer pressure can cause valuable outcomes in online review behaviors.