[Reference] AI-based text generation model for improving detection of manipulated reviews by solving the class imbalance problem

date
2023.09.19
name
IBA lab 관리자
e-mail
view
13

1. Title

 "AI-based text generation model for improving detection of manipulated reviews by solving the class imbalance problem"


2. References

  1. Aggarwal, C. C., & Zhai, C. (2012). An introduction to text mining. In Mining text data (pp. 1-10). Springer, Boston, MA.
  2. Anderson, E. T., & Simester, D. I. (2014). Reviews without a purchase: Low ratings, loyal customers, and deception. Journal of Marketing Research, 51(3), 249-269.
  3. Banerjee, S., Bhattacharyya, S., & Bose, I. (2017). Whose online reviews to trust? Understanding reviewer trustworthiness and its impact on business. Decision Support Systems, 96, 17-26
  4. Bilal, M., & Almazroi, A. A. (2022). Effectiveness of Fine-Tuned BERT Model in Classification of Helpful and Unhelpful Online Customer Reviews. Electronic Commerce Research, 1-21.
  5. Cao, Q., Duan, W., & Gan, Q. (2011). Exploring determinants of voting for the “helpfulness” of online user reviews: A text mining approach. Decision Support Systems, 50(2), 511-521.
  6. Chen, L. S., & Lin, J. Y. (2013, July). A study on review manipulation classification using decision tree. In 2013 10th international conference on service systems and service management (pp. 680-685). IEEE.
  7. Cheng, Y. H., & Ho, H. Y. (2015). Social influence's impact on reader perceptions of online reviews. Journal of Business Research, 68(4), 883-887.
  8. 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), 1-24.
  9. DePaulo, B. M., Kashy, D. A., Kirkendol, S. E., Wyer, M. M., & Epstein, J. A. (1996). Lying in everyday life. Journal of personality and social psychology, 70(5), 979.
  10. D?az, S. S., Shaik, J. M. M., & Santofimio, J. C. G. (2018, November). Intelligent execution of behaviors in a nao robot exposed to audiovisual stimulus. In 2018 IEEE 2nd Colombian Conference on Robotics and Automation (CCRA) (pp. 1-6). IEEE.
  11. Dwivedi, Y. K., Ismagilova, E., Hughes, D. L., Carlson, J., Filieri, R., Jacobson, J., ... & Wang, Y. (2021). Setting the future of digital and social media marketing research: Perspectives and research propositions. International Journal of Information Management, 59, 102168.
  12. Eslami, S. P., & Ghasemaghaei, M. (2018). Effects of online review positiveness and review score inconsistency on sales: A comparison by product involvement. Journal of Retailing and Consumer Services, 45, 74-80.
  13. Filieri, R. (2015). What makes online reviews helpful? A diagnosticity-adoption framework to explain informational and normative influences in e-WOM. Journal of business research, 68(6), 1261-1270.
  14. Gobi, N., & Rathinavelu, A. (2019). Analyzing cloud based reviews for product ranking using feature based clustering algorithm. Cluster Computing, 22(3), 6977-6984.
  15. G?ssling, S., Hall, C. M., & Andersson, A. C. (2018). The manager's dilemma: a conceptualization of online review manipulation strategies. Current Issues in Tourism, 21(5), 484-503.
  16. He, S., Hollenbeck, B., & Proserpio, D. (2022). The market for fake reviews. Marketing Science.
  17. Hu, N., Bose, I., Koh, N. S., & Liu, L. (2012). Manipulation of online reviews: An analysis of ratings, readability, and sentiments. Decision support systems, 52(3), 674-684.
  18. Hu, N., Liu, L., & Sambamurthy, V. (2011). Fraud detection in online consumer reviews. Decision Support Systems, 50(3), 614-626.
  19. Ismagilova, E., Slade, E., Rana, N. P., & Dwivedi, Y. K. (2020). The effect of characteristics of source credibility on consumer behaviour: A meta-analysis. Journal of Retailing and Consumer Services, 53, 101736.
  20. Lau, R. Y., Liao, S. Y., Kwok, R. C. W., Xu, K., Xia, Y., & Li, Y. (2012). Text mining and probabilistic language modeling for online review spam detection. ACM Transactions on Management Information Systems (TMIS), 2(4), 1-30.
  21. Lim, E. P., Nguyen, V. A., Jindal, N., Liu, B., & Lauw, H. W. (2010, October). Detecting product review spammers using rating behaviors. In Proceedings of the 19th ACM international conference on Information and knowledge management (pp. 939-948).
  22. Luca, M. (2016). Reviews, reputation, and revenue: The case of Yelp. com. Com (March 15, 2016). Harvard Business School NOM Unit Working Paper, (12-016).
  23. Majumdar, S., Kulkarni, D., & Ravishankar, C. V. (2007, May). Addressing click fraud in content delivery systems. In IEEE INFOCOM 2007-26th IEEE International Conference on Computer Communications (pp. 240-248). IEEE.
  24. Mayzlin, D., Dover, Y., & Chevalier, J. (2014). Promotional reviews: An empirical investigation of online review manipulation. American Economic Review, 104(8), 2421-55.
  25. Mikolov, T., Le, Q. V., & Sutskever, I. (2013). Exploiting similarities among languages for machine translation. arXiv preprint arXiv:1309.4168.
  26. Nunamaker Jr, J. F., Burgoon, J. K., & Giboney, J. S. (2016). Information systems for deception detection. Journal of Management Information Systems, 33(2), 327-331.
  27. Ohana, B., & Tierney, B. (2009). Sentiment classification of reviews using SentiWordNet. Proceedings of IT&T, 8.
  28. Ott, M., Choi, Y., Cardie, C., & Hancock, J. T. (2011). Finding deceptive opinion spam by any stretch of the imagination. arXiv preprint arXiv:1107.4557.
  29. Salminen, J., Kandpal, C., Kamel, A. M., Jung, S. G., & Jansen, B. J. (2022). Creating and detecting fake reviews of online products. Journal of Retailing and Consumer Services, 64, 102771.
  30. Shmueli, G., Patel, N. R., & Bruce, P. C. (2011). Data mining for business intelligence: Concepts, techniques, and applications in Microsoft Office Excel with XLMiner. John Wiley and Sons.
  31. Tian, K., Shao, M., Wang, Y., Guan, J., & Zhou, S. (2016). Boosting compound-protein interaction prediction by deep learning. Methods, 110, 64-72.
  32. Tsikerdekis, M., & Zeadally, S. (2014). Online deception in social media. Communications of the ACM, 57(9), 72-80.
  33. Weisberg, J., Te'eni, D., & Arman, L. (2011). Past purchase and intention to purchase in e­commerce: The mediation of social presence and trust. Internet research.
  34. Yelp Trust & Safety. Trust & Safety Report. https://trust.yelp.com/trust-and-safety-report/
file
there is no file