Identifying unreliable online hospitality reviews with biased user-given ratings: A deep learning forecasting approach

Tianxiang Zheng, Feiran Wu, Rob Law, Qihang Qiu, Rong Wu

Research output: Journal article publicationJournal articleAcademic researchpeer-review

5 Citations (Scopus)

Abstract

This study considers the review reliability problem by identifying biased user-given ratings through rating prediction on the basis of the textual content. Deep learning approaches were introduced to investigate the textual review and validate the effect of rating prediction using a dataset collected from Yelp. The definition of “biased rating” was clarified and influenced the matching rules. The approach obtains high performance on a total of 1,000,000 reviews for prediction, with user-given ratings as the benchmark. Using the revealed biased ratings, unreliable reviews were detected by combining the results of several deep learning kernels. Findings shed light on understanding review quality by distinguishing biased ratings and unreliable reviews that may cause inconsistency and ambiguity to readers. Hence, theoretical and managerial areas for social media analytics are enriched on the basis of online review meta-data in hospitality and tourism.

Original languageEnglish
Article number102658
JournalInternational Journal of Hospitality Management
Volume92
DOIs
Publication statusPublished - Jan 2021

Keywords

  • Deep learning
  • Information quality
  • Online customer review
  • Review rating prediction
  • Review reliability

ASJC Scopus subject areas

  • Tourism, Leisure and Hospitality Management
  • Strategy and Management

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