TY  - BOOK
AU  - Zheng, Tianxiang
AU  - Wu, Feiran
AU  - Law, Rob
AU  - Qiu, Qihang
AU  - Wu, Rong 
TI  - Identifying unreliable online hospitality reviews with biased user-given ratings: a deep learning forecasting approach 
SN  - 0278-4319
PY  - 2020///
CY  - Amsterdam
PB  - Elsevier Ltd.
KW  - ONLINE CUSTOMER REVIEW
KW  - REVIEW RELIABILITY
KW  - REVIEW RATING PREDICTION
KW  - DEEP LEARNING
KW  - INFORMATION QUALITY
N1  - Includes bibliographical references (page 9)
N2  - 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
UR  - https://doi.org/10.1016/j.ijhm.2020.102658
ER  -