A fake review identification framework considering the suspicion degree of reviews with time burst characteristics

Author:

Wang Ning,Yang Jun,Kong Xuefeng,Gao Ying

Funder

National Natural Science Foundation of China

Fundamental Research Funds for the Central Universities

Publisher

Elsevier BV

Subject

Artificial Intelligence,Computer Science Applications,General Engineering

Reference51 articles.

1. Akoglu, L., Chandy, R., & Faloutsos, C. (2013). Opinion Fraud Detection in Online Reviews by Network Effects. 7th International AAAI Conference on Weblogs and Social Media (ICWSM), Boston, Massachusetts, USA.

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4. A theoretical framework to identify authentic online reviews;Banerjee;Online Information Review,2014

5. Banerjee, S., Chua, A. Y. K., & Kim, J. (2017). Don't be deceived: using linguistic analysis to learn how to discern online review authenticity. Journal of the Association for Information Science and Technology, Vol. 68(No.6), 1525-1538. doi: 10.1002/asi.23784.

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