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Spam review detection using LSTM autoencoder: an unsupervised approach

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Abstract

The review of online products or services is becoming a major factor in the user’s purchasing decisions. The popularity and influence of online reviews attract spammers who intend to elevate their products or services by writing positive reviews for them and lowering the business of others by writing negative reviews. Traditionally, the spam review identification task is seen as a two-class classification problem. The classification approach requires a labelled dataset to train a model for the environment it is working on. The unavailability of the labelled dataset is a major limitation in the classification approach. To overcome the problem of the labelled dataset, we propose an unsupervised learning model combining long short-term memory (LSTM) networks and autoencoder (LSTM-autoencoder) to distinguish spam reviews from other real reviews. The said model is trained to learn the patterns of real review from the review’s textual details without any label. The experimental results show that our model is able to separate the real and spam review with good accuracy.

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References

  1. Akoglu, L., Chandy, R., & Faloutsos, C. (2013). Opinion fraud detection in online reviews by network effects. ICWSM, 13, 2–11.

    Google Scholar 

  2. Alberto, T. C., Lochter, J. V., & Almeida, T. A. (2015). Tubespam: Comment spam filtering on youtube. In 2015 IEEE 14th international conference on machine learning and applications (ICMLA) (pp. 138–143). IEEE.

  3. Banerjee, S., Chua, A. Y., & Kim, J.-J. (2015). Using supervised learning to classify authentic and fake online reviews. In Proceedings of the 9th international conference on ubiquitous information management and communication (p.  88). ACM.

  4. Chua, A. Y., & Banerjee, S. (2013). Reliability of reviews on the internet: The case of tripadvisor. In World congress on engineering & computer science (pp. 453–457). York.

  5. Crawford, M., Khoshgoftaar, T. M., Prusa, J. D., Richter, A. N., & Al Najada, H. (2015). Survey of review spam detection using machine learning techniques. Journal of Big Data, 2(1), 23.

    Article  Google Scholar 

  6. Dong, M., Yao, L., Wang, X., Benatallah, B., Huang, C., & Ning, X. (2018). Opinion fraud detection via neural autoencoder decision forest. arXiv:1805.03379.

  7. Feng, S., Banerjee, R., & Choi, Y. (2012). Syntactic stylometry for deception detection. In Proceedings of the 50th annual meeting of the association for computational linguistics: Short papers (Vol. 2, pp. 171–175). Association for Computational Linguistics.

  8. Heydari, A., ali Tavakoli, M., Salim, N., & Heydari, Z. (2015). Detection of review spam: A survey. Expert Systems with Applications, 42(7), 3634–3642.

    Article  Google Scholar 

  9. Heydari, A., Tavakoli, M., & Salim, N. (2016). Detection of fake opinions using time series. Expert Systems with Applications, 58, 83–92.

    Article  Google Scholar 

  10. Jindal, N., & Liu, B. (2007a). Analyzing and detecting review spam. In ICDM (pp. 547–552). IEEE.

  11. Jindal, N., & Liu, B. (2007b). Review spam detection. In Proceedings of the 16th international conference on world wide web (pp. 1189–1190). ACM.

  12. Jindal, N., & Liu, B. (2008). Opinion spam and analysis. In Proceedings of the 2008 international conference on web search and data mining (pp. 219–230). ACM.

  13. Jindal, N., Liu, B., & Lim, E. -P. (2010). Finding unusual review patterns using unexpected rules. In Proceedings of the 19th ACM international conference on information and knowledge management (pp. 1549–1552). ACM.

  14. Kolhar, M. (2018). E-commerce review system to detect false reviews. Science and Engineering Ethics, 24, 1577–1588. https://doi.org/10.1007/s11948-017-9959-2.

    Article  Google Scholar 

  15. Lai, C., Xu, K., Lau, R. Y., Li, Y., & Jing, L. (2010). Toward a language modeling approach for consumer review spam detection. In 2010 IEEE 7th international conference on e-business engineering (ICEBE) (pp. 1–8). IEEE.

  16. Lai, C., Xu, K., Lau, R. Y., Li, Y., & Song, D. (2010). High-order concept associations mining and inferential language modeling for online review spam detection. In 2010 IEEE international conference on data mining workshops (ICDMW) (pp. 1120–1127). IEEE.

  17. Lau, R. Y., Liao, S., Kwok, R. C. W., Xu, K., Xia, Y., & Li, Y. (2011). Text mining and probabilistic language modeling for online review spam detecting. ACM Transactions on Management Information Systems, 2(4), 1–30.

    Article  Google Scholar 

  18. Li, H., Fei, G., Wang, S., Liu, B., Shao, W., Mukherjee, A., & Shao, J. (2017). Bimodal distribution and co-bursting in review spam detection. In Proceedings of the 26th international conference on world wide web (pp. 1063–1072). International World Wide Web Conferences Steering Committee.

  19. Li, J., Ott, M., Cardie, C., & Hovy, E. H. (2014). Towards a general rule for identifying deceptive opinion spam. ACL, 1, 1566–1576.

    Google Scholar 

  20. Li, L., Qin, B., Ren, W., & Liu, T. (2017). Document representation and feature combination for deceptive spam review detection. Neurocomputing, 254, 33–41.

    Article  Google Scholar 

  21. Li, Y., Lin, Y., Zhang, J., Li, J., & Zhao, L. (2015). Highlighting the fake reviews in review sequence with the suspicious contents and behaviours. Journal of Information & Computational Science, 12(4), 1615–1627.

    Article  Google Scholar 

  22. Lim, E. -P., Nguyen, V. -A., Jindal, N., Liu, B., & Lauw, H. W. (2010). Detecting product review spammers using rating behaviors. In Proceedings of the 19th ACM international conference on information and knowledge management (pp. 939–948). ACM.

  23. Lu, Y., Zhang, L., Xiao, Y., & Li, Y. (2013). Simultaneously detecting fake reviews and review spammers using factor graph model. In Proceedings of the 5th annual ACM web science conference (pp. 225–233). ACM.

  24. Markines, B., Cattuto, C., & Menczer, F. (2009). Social spam detection. In Proceedings of the 5th international workshop on adversarial information retrieval on the web (pp. 41–48). ACM.

  25. Mukherjee, A., Kumar, A., Liu, B., Wang, J., Hsu, M., Castellanos, M., & Ghosh, R. (2013). Spotting opinion spammers using behavioral footprints. In Proceedings of the 19th ACM SIGKDD international conference on knowledge discovery and data mining (pp. 632–640). ACM.

  26. Mukherjee, A., Liu, B., & Glance, N. (2012). Spotting fake reviewer groups in consumer reviews. In Proceedings of the 21st international conference on world wide web (pp. 191–200). ACM.

  27. Ott, M., Choi, Y., Cardie, C., Hancock, J. T. (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 (Vol. 1, pp. 309–319). Association for Computational Linguistics.

  28. Rastogi, A., & Mehrotra, M. (2017). Opinion spam detection in online reviews. Journal of Information & Knowledge Management, 16(04), 1750036.

    Article  Google Scholar 

  29. Rayana, S., & Akoglu, L. (2015). Collective opinion spam detection: Bridging review networks and metadata. In Proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining (pp. 985–994). ACM.

  30. Ren, Y., & Ji, D. (2017). Neural networks for deceptive opinion spam detection: An empirical study. Information Sciences, 385, 213–224.

    Article  Google Scholar 

  31. Rowland, C. H. (2002). Intrusion detection system. US Patent 6,405,318.

  32. Roy, P. K., Singh, J. P., & Banerjee, S. (2020). Deep learning to filter SMS spam. Future Generation Computer Systems, 102, 524–533.

    Article  Google Scholar 

  33. Saumya, S., & Singh, J. P. (2018). Detection of spam reviews: A sentiment analysis approach. CSI Transactions on ICT, 6(2), 137–148.

    Article  Google Scholar 

  34. Saumya, S., Singh, J. P., Baabdullah, A. M., Rana, N. P., & Dwivedi, Y. K. (2018). Ranking online consumer reviews. Electronic Commerce Research and Applications, 29, 78–89.

    Article  Google Scholar 

  35. Saumya, S., Singh, J. P., & Dwivedi, Y. K. (2019). Predicting the helpfulness score of online reviews using convolutional neural network. Soft Computing. https://doi.org/10.1007/s00500-019-03851-5.

    Article  Google Scholar 

  36. Singh, J. P., Irani, S., Rana, N. P., Dwivedi, Y. K., Saumya, S., & Roy, P. K. (2017). Predicting the “helpfulness” of online consumer reviews. Journal of Business Research, 70, 346–355.

    Article  Google Scholar 

  37. Sundermeyer, M., Schlüter, R., & Ney, H. (2012). LSTM neural networks for language modeling. In Thirteenth annual conference of the international speech communication association.

  38. Wang, G., Xie, S., Liu, B., & Yu, P. S. (2012). Identify online store review spammers via social review graph. ACM Transactions on Intelligent Systems and Technology (TIST), 3(4), 61.

    Google Scholar 

  39. Wang, Z., Gu, S., & Xu, X. (2018). GSLDA: LDA-based group spamming detection in product reviews. Applied Intelligence, 48, 3094–3107. https://doi.org/10.1007/s10489-018-1142-1.

    Article  Google Scholar 

  40. Wang, Z., Hou, T., Song, D., Li, Z., & Kong, T. (2016). Detecting review spammer groups via bipartite graph projection. The Computer Journal, 59(6), 861–874.

    Article  Google Scholar 

  41. Xie, S., Wang, G., Lin, S., & Yu, P. S. (2012). Review spam detection via temporal pattern discovery. In Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 823–831). ACM.

  42. Zhang, W., Du, Y., Yoshida, T., & Wang, Q. (2018). DRI-RCNN: An approach to deceptive review identification using recurrent convolutional neural network. Information Processing & Management, 54(4), 576–592.

    Article  Google Scholar 

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Acknowledgements

The author would like to acknowledge the Ministry of Electronics and Information Technology (MeitY), Government of India for supporting the financial assistant during research work through “Visvesvaraya Ph.D. Scheme for Electronics and IT”.

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Authors and Affiliations

  1. Indian Institute of Information Technology Dharwad, Dharwad, India

    Sunil Saumya

  2. National Institute of Technology Patna, Patna, India

    Jyoti Prakash Singh

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  1. Sunil Saumya
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Correspondence to Sunil Saumya.

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Sunil Saumya was formerly at National Institute of Technology Patna.

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Saumya, S., Singh, J.P. Spam review detection using LSTM autoencoder: an unsupervised approach. Electron Commer Res 22, 113–133 (2022). https://doi.org/10.1007/s10660-020-09413-4

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  • DOI: https://doi.org/10.1007/s10660-020-09413-4

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