PACIS 2020 Proceedings


The proliferation of social media platforms flourishes research on helpful online reviews. Prior studies have ubiquitously taken subjective indicators to measure online review helpfulness, such as the voted reviews and reviews with an emotional tendency. By highlighting helpful reviews, researchers strive to extricate consumers from the explosive growth amount of post-purchase information. In this study, we theoretically reformulate the consumer-oriented online review helpfulness as three indicators, including effectiveness (i.e., product-specific), representativeness or objectivity (i.e., identical distribution with original review set), and semantic diversity (for personalized information demand). Moreover, we design a novel disclosure pattern-wise method to coordinate the three indicators for enhancing helpful review extraction. Experiments on more than 2 million of hotel reviews manifest the superiority of our proposed method for balancing the trade-off among different review helpfulness indicators, in contrast to conventional helpful review extraction methods.



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