Fake Review Detection Using Machine Learning Techniques
| dc.contributor.author | Bari, Sadat Shahriar | |
| dc.contributor.author | Sakib, Robiul Ahammed | |
| dc.contributor.author | Nico, Nabil Hossain | |
| dc.date.accessioned | 2023-03-23T10:19:04Z | |
| dc.date.available | 2023-03-23T10:19:04Z | |
| dc.date.issued | 2022-05-30 | |
| dc.description | Supervised by Ms. Lutfun Nahar Lota, Assistant Professor. Department of Computer Science and Engineering(CSE), Islamic University of Technology (IUT) Board Bazar, Gazipur-1704, Bangladesh. This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2022. | en_US |
| dc.description.abstract | Nowadays, review sites are increasingly confronted with the spread of disinformation, for example, opinion spam, which aims to promote or harm certain target businesses, by simultaneously deceiving the human readers. For this reason, over the past years, several data-driven approaches have been proposed to assess the credibility of user-generated content delivered through social media in the form of online reviews. Linked to both review and reviewers, as well as the network structure that links separate entities at the review site.This article aims to provide an analysis of various machine learning methods and deep learning methods for analyzing fake user review detection on bangla languages based on the reviewer and review-centric features.Additionally, this work offers to provide a synthesized dataset for fake user review detection in the Bangla language | en_US |
| dc.identifier.citation | [1] M. Loiselle, “3 important statistics that show how reviews influence consumers,” 2021. [Online]. Available: https://www.dixa.com/blog/ 3-important-statistics-that-show-how-reviews-influence-consumers/ [2] J. Fontanarava, G. Pasi, and M. Viviani, “Feature analysis for fake review detection through supervised classification,” pp. 658–666, 2017. [3] A. Elmogy, U. Tariq, A. Mohammed, and A. Ibrahim, “Fake reviews detection using supervised machine learning,” International Journal of Advanced Computer Science and Applications, vol. 12, 01 2021. [4] R. Barbado, O. Araque, and C. Iglesias, “A framework for fake review detection in online consumer electronics retailers,” Information Processing and Management, vol. 56, 03 2019. [5] A. Mukherjee, A. Kumar, B. Liu, J. Wang, M. Hsu, M. Castellanos, and R. Ghosh, “Spotting opinion spammers using behavioral footprints,” pp. 632–640, 08 2013. [6] G. Fei, A. Mukherjee, B. Liu, M. Hsu, M. Castellanos, and R. Ghosh, “Exploiting burstiness in reviews for review spammer detection,” Proceedings of the 7th International Conference on Weblogs and Social Media, ICWSM 2013, pp. 175–184, 01 2013. [7] A. Mukherjee, V. Venkataraman, B. Liu, and N. S. Glance, “Fake review detection : Classification and analysis of real and pseudo reviews,” 2013. [8] S. Rayana and L. Akoglu, “Collective opinion spam detection: Bridging review networks and metadata,” p. 985–994, 2015. [Online]. Available: https://doi.org/10.1145/2783258.2783370 [9] N. Chawla, K. Bowyer, L. Hall, and W. Kegelmeyer, “Smote: Synthetic minority over-sampling technique,” J. Artif. Intell. Res. (JAIR), vol. 16, pp. 321–357, 06 2002. 20 | en_US |
| dc.identifier.uri | http://hdl.handle.net/123456789/1782 | |
| dc.language.iso | en | en_US |
| dc.publisher | Department of Computer Science and Engineering(CSE), Islamic University of Technology(IUT), Board Bazar, Gazipur, Bangladesh | en_US |
| dc.subject | Fake reviews, machine learning, reviewer centric , review centric | en_US |
| dc.title | Fake Review Detection Using Machine Learning Techniques | en_US |
| dc.type | Thesis | en_US |
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