Acknowledgement
This work was supported by a 2-Year Research Grant of Pusan National University.
References
- 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. https://doi.org/10.1509/jmr.13.0209
- Ball, L., & Elworthy, J. (2014). Fake or real? The computational detection of online deceptive text. Journal of Marketing Analytics, 2(3), 187-201. https://doi.org/10.1057/jma.2014.15
- Banerjee, S., & Chua, A. Y. (2014). A theoretical framework to identify authentic online reviews. Online Information Review.
- 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 https://doi.org/10.1016/j.dss.2017.01.006
- 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. https://doi.org/10.1016/j.dss.2010.11.009
- 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.
- 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. https://doi.org/10.1016/j.jbusres.2014.11.046
- 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. https://doi.org/10.1186/s40537-014-0007-7
- Douzas, G., Bacao, F., & Last, F. (2018). Improving imbalanced learning through a heuristic oversampling method based on k-means and SMOTE. Information Sciences, 465, 1-20. https://doi.org/10.1016/j.ins.2018.06.056
- 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. https://doi.org/10.1016/j.ijinfomgt.2020.102168
- 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. https://doi.org/10.1016/j.jretconser.2018.08.003
- Fernandez, A., Garcia, S., Luengo, J., Bernado-Mansilla, E., & Herrera, F. (2010). Genetics-based machine learning for rule induction: state of the art, taxonomy, and comparative study. IEEE Transactions on Evolutionary Computation, 14(6), 913-941. https://doi.org/10.1109/TEVC.2009.2039140
- 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. https://doi.org/10.1016/j.jbusres.2014.11.006
- Gobi, N., & Rathinavelu, A. (2019). Analyzing cloud based reviews for product ranking using feature based clustering algorithm. Cluster Computing, 22(3), 6977-6984. https://doi.org/10.1007/s10586-018-1996-3
- Gossling, 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. https://doi.org/10.1080/13683500.2015.1127337
- He, S., Hollenbeck, B., & Proserpio, D. (2022). The market for fake reviews. Marketing Science.
- 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. https://doi.org/10.1016/j.dss.2011.11.002
- Hu, N., Liu, L., & Sambamurthy, V. (2011). Fraud detection in online consumer reviews. Decision Support Systems, 50(3), 614-626. https://doi.org/10.1016/j.dss.2010.08.012
- 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. https://doi.org/10.1016/j.jretconser.2019.01.005
- Jalther, D., & Priya, G. (2019). Reputation reporting system using text based classification. Int. J. Innov. Technol. and Expl. Eng., 8(8), 1555-1558.
- Khurshid, F., Zhu, Y., Xu, Z., Ahmad, M., & Ahmad, M. (2019). Enactment of ensemble learning for review spam detection on selected features. International Journal of Computational Intelligence Systems, 12(1), 387-394. https://doi.org/10.2991/ijcis.2019.125905655
- Kim, J., & Kwahk, K.-Y. (2022). Class Imbalance Resolution Method and Classification Algorithm Suggesting Based on Dataset Type Segmentation. Journal of Intelligence and Information Systems, 28(3), 23-43. https://doi.org/10.13088/JIIS.2022.28.3.023
- Kim, M. J., Kang, D. K., & Kim, H. B. (2015). Geometric mean based boosting algorithm with over-sampling to resolve data imbalance problem for bankruptcy prediction. Expert Systems with Applications, 42(3), 1074-1082. https://doi.org/10.1016/j.eswa.2014.08.025
- Kotsiantis, S., Kanellopoulos, D., & Pintelas, P. (2006). Handling imbalanced datasets: A review. GESTS international transactions on computer science and engineering, 30(1), 25-36.
- Kumar, A., Gopal, R. D., Shankar, R., & Tan, K. H. (2022). Fraudulent review detection model focusing on emotional expressions and explicit aspects: investigating the potential of feature engineering. Decision Support Systems, 155, 113728. https://doi.org/10.1016/j.dss.2021.113728
- Li, H., Li, J., Chang, P. C., & Sun, J. (2013). Parametric prediction on default risk of Chinese listed tourism companies by using random oversampling, isomap, and locally linear embeddings on imbalanced samples. International Journal of Hospitality Management, 35, 141-151. https://doi.org/10.1016/j.ijhm.2013.06.006
- Li, L., Qin, B., Ren, W., & Liu, T. (2017). Document representation and feature combination for deceptive spam review detection. Neurocomputing, 254, 33-41. https://doi.org/10.1016/j.neucom.2016.10.080
- Li, X., Yun, H., Li, Q., & Kim, J. (2022). A multi-channel CNN based online review helpfulness prediction model. Journal of Intelligence and Information Systems, 28(2), 171-189. https://doi.org/10.13088/JIIS.2022.28.2.171
- Liang, Y., & Zhu, K. (2018, April). Automatic generation of text descriptive comments for code blocks. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 32, No. 1).
- 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).
- Liu, Y., Pang, B., & Wang, X. (2019). Opinion spam detection by incorporating multimodal embedded representation into a probabilistic review graph. Neurocomputing, 366, 276-283. https://doi.org/10.1016/j.neucom.2019.08.013
- Lopez, V., Fernandez, A., Garcia, S., Palade, V., & Herrera, F. (2013). An insight into classification with imbalanced data: Empirical results and current trends on using data intrinsic characteristics. Information sciences, 250, 113-141. https://doi.org/10.1016/j.ins.2013.07.007
- 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).
- 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.
- Mayzlin, D., Dover, Y., & Chevalier, J. (2014). Promotional reviews: An empirical investigation of online review manipulation. American Economic Review, 104(8), 2421-55. https://doi.org/10.1257/aer.104.8.2421
- Mouratidis, D., Nikiforos, M. N., & Kermanidis, K. L. (2021). Deep learning for fake news detection in a pairwise textual input schema. Computation, 9(2), 20. https://doi.org/10.3390/computation9020020
- 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. https://doi.org/10.1080/07421222.2016.1205928
- 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. https://doi.org/10.48550/arXiv.1107.4557
- Park, Y.-J., & Kim, K.-j. (2017). Impact of Semantic Characteristics on Perceived Helpfulness of Online Reviews. Journal of Intelligence and Information Systems, 23(3), 29-44. https://doi.org/10.13088/JIIS.2017.23.3.029
- Rajamohana, S. P., & Umamaheswari, K. (2018). Hybrid approach of improved binary particle swarm optimization and shuffled frog leaping for feature selection. Computers & Electrical Engineering, 67, 497-508. https://doi.org/10.1016/j.compeleceng.2018.02.015
- Rajamohana, S. P., Umamaheswari, K., & Abirami, B. (2017). Performance analysis of iBPSO and BFPA based feature selection techniques for improving classification accuracy in review spam detection. Appl. Math, 11(4), 1149-1153.
- Ren, Y., & Ji, D. (2017). Neural networks for deceptive opinion spam detection: An empirical study. Information Sciences, 385, 213-224. https://doi.org/10.1016/j.ins.2017.01.015
- 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. https://doi.org/10.1016/j.jretconser.2021.102771
- Scott, K. (2020). Microsoft teams up with OpenAI to exclusively license GPT-3 language model. Official Microsoft Blog.
- 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.
- Suh, Y., Yu, J., Mo, J., Song, L., & Kim, C. (2017). A comparison of oversampling methods on imbalanced topic classification of Korean news articles. Journal of Cognitive Science, 18(4), 391-437. https://doi.org/10.17791/jcs.2017.18.4.391
- Tian, K., Shao, M., Wang, Y., Guan, J., & Zhou, S. (2016). Boosting compound-protein interaction prediction by deep learning. Methods, 110, 64-72. https://doi.org/10.1016/j.ymeth.2016.06.024
- Veganzones, D., & Severin, E. (2018). An investigation of bankruptcy prediction in imbalanced datasets. Decision Support Systems, 112, 111-124. https://doi.org/10.1016/j.dss.2018.06.011
- Weisberg, J., Te'eni, D., & Arman, L. (2011). Past purchase and intention to purchase in e-commerce: The mediation of social presence and trust. Internet research.
- Yelp Trust & Safety. Trust & Safety Report. https://trust.yelp.com/trust-and-safety-report/
- Zhang, D., Zhou, L., Kehoe, J. L., & Kilic, I. Y. (2016). What online reviewer behaviors really matter? Effects of verbal and nonverbal behaviors on detection of fake online reviews. Journal of Management Information Systems, 33(2), 456-481. https://doi.org/10.1080/07421222.2016.1205907