Chih-Chien Wang
ABSTRACT
Some unethical companies may hire workers (fake review spammers) to write reviews to influence consumers' purchasing decisions. However, it is not easy for consumers to distinguish real reviews posted by ordinary users or fake reviews post by fake review spammers. In this current study, we attempt to use Long Short-Term Memory (LSTM) Recurrent Neural Network (RNN) framework to detect spammers. In the current, we used a real case of fake review in Taiwan, and compared the analytical results of the current study with results of previous literature. We found that the LSTM method was more effective than Support Vector Machine (SVM) for detecting fake reviews. We concluded that deep learning could be use to detect fake reviews.
- Mitchell, V. W. and McGoldrick, P.J. 1996. Consumer's risk-reduction strategies: a review and synthesis. The International Review of Retail, Distribution and Consumer Research, 6, 1, 1--33.Google Scholar
Cross Ref
- Jindal, N. and Liu, B. 2008. Opinion spam and analysis, in Proceedings of the 2008 International Conference on Web Search and Data Mining. ACM: Palo Alto, California, USA. 219--230. Google ScholarDigital Library
- Ott, M., et al., 2011. Finding deceptive opinion spam by any stretch of the imagination, in Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - 1, Association for Computational Linguistics: Portland, Oregon. 309--319. Google ScholarDigital Library
- Algur, S., et al., 2010. Conceptual level similarity measure based review spam detection. 416--423.Google Scholar
- Chen, Y.-R. and Chen. H.-H. 2015. Opinion spam detection in web forum: a real case study, in Proceedings of the 24th International Conference on World Wide Web. International World Wide Web Conferences Steering Committee. Google ScholarDigital Library
- Wang, C.-C., Day, M.-Y., and Lin. Y.-R. 2016. A Real Case Analytics on Social Network of Opinion Spammers, in Information Reuse and Integration (IRI), 2016 IEEE 17th International Conference on Information Reuse and Integration (IRI). IEEE.Google ScholarCross Ref
- Lau, R.Y., et al., 2011. Text mining and probabilistic language modeling for online review spam detecting. ACM Transactions on Management Information Systems, 2, 4, 1--30. Google ScholarDigital Library
- Ren, Y., Ji, D., and Zhang, H. 2014. Positive Unlabeled Learning for Deceptive Reviews Detection, in EMNLP.Google Scholar
- Hinton, G.E. and Salakhutdinov, R.R. 2006. Reducing the dimensionality of data with neural networks. Science, 313, 5786, 504--507.Google Scholar
- Bengio, Y., Simard, P., and Frasconi, P. 1994. Learning long-term dependencies with gradient descent is difficult. IEEE transactions on neural networks, 5, 2, 157--166. Google ScholarDigital Library
- Hochreiter, S. and Schmidhuber, J. 1997. Long short-term memory. Neural computation, 9, 8, 1735--1780. Google ScholarDigital Library
- Hinton, G. E., et al., 2012. Improving neural networks by preventing co-adaptation of feature detectors. arXiv preprint arXiv: 1207.0580.Google Scholar
Index Terms
- Detecting spamming reviews using long short-term memory recurrent neural network framework
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