Detecting Online Fake Reviews Using Supervised and Semi Supervised Learning

References

[1] Rakibul Hassan, Md. Rabiul Islam “Detection of fake online reviews using semisupervised and supervised learning” 2019 International Conference on Electrical, Computer and Communication Engineering (ECCE), 7-9 February, 2019 .

[2] A. Heydari, M. A. Tavakoli, N. Salim, and Z. Heydari, ”Detection of review spam: a survey”, Expert Systems with Applications, vol. 42, no. 7, pp. 3634–3642, 2015 .

[3] J. Li, M. Ott, C. Cardie and E. Hovy, “Towards a General Rule for Identifying Deceptive Opinion Spam,” in Proceedings of 52nd Annual Meeting of the Association for Baltimore, MD, USA, vol. 1, no. 11, pp.

[4] Chengai Sun, Qiaolin Du and Gang Tian, “Exploiting Product Related Review Features for Fake Review Detection,” Mathematical Problems in Engineering, 2016.

[5] J. C.S. Reis, A. Correia, F. Murai, A. Veloso, and F. Benevenuto, “Supervised Learning for Fake News Detection,” IEEE Intelligent Systems, vol. 34, no. 2, pp. 76-81, May 2019.

[6] B. Wagh, J. V. Shinde and P. A. Kale, Twitter Sentiment Analysis Using NLTK and Machine Learning Techniques,” International Journal of Emerging Research in Management and Technology, vol. 6, no. 12, pp. 37-44, December 2017.

[7] E. I. Elmurngi and A.Gherbi, “Unfair Reviews Detection on Amazon Reviews using Sentiment Analysis with Supervised Learning Techniques,” Journal of Computer Science, vol. 14, no. 5, pp. 714– 26, June 2018.

[8] J. K. Rout, A. Dalmia, and K.-K. R. Choo, “Revisiting semi-supervised learning for online deceptive review detection,” IEEE Access, Vol.5, pp. 1319–1327, 2017

[9] N. O’Brien, “Machine Learning for Detection of Fake News,”[Online].Availablehttps://dspace.mit.edu/bitstream/handle/1721.1/119727/1078649610-MIT.pdf [Accessed: November 2018].

[10] N. Jindal and B. Liu., “Opinion spam and analysis”, Proceedings of the international conference on Web search and web data mining - WSDM 08 (2008), ACM, pp. 219 –230,2008.

@article{1712519,
      author = {Akshaya D, Daniya Begum, Nisarga H N, Syed Saifulla, Rekha D.},
      title = {Detecting Online Fake Reviews Using Supervised and Semi Supervised Learning},
      journal = {Iconic Research And Engineering Journals},
      year = {2025},
      volume = {9},
      number = {6},
      pages = {254-257},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1712519.pdf},
      abstract = {Online reviews play a crucial role in influencing consumer decisions, which makes them a target for manipulation through fake or misleading feedback. Detecting such deceptive reviews is challenging because fraudulent content is often written to closely resemble genuine opinions. This project presents a hybrid approach for *detecting online fake reviews using supervised and semisupervised machine learning techniques*. The system  abelled both linguistic features and reviewer behavior to classify reviews as genuine or deceptive. Supervised learning models such as Support Vector Machines, Logistic Regression, and Random Forest are trained on  abelled datasets to establish a strong baseline. To address the scarcity of high-quality  abelled data, semisupervised methods?including Self-Training and Label Propagation?are integrated to utilize large amounts of unlabeled reviews. This combination enhances model robustness and improves detection accuracy in real-world scenarios.   Experimental results demonstrate that the semi-supervised models significantly improve performance, especially when  abelled data is limited. The proposed hybrid approach offers an effective and scalable solution for identifying deceptive content, helping e-commerce platforms protect customers and maintain trust.},
      month = {December},
      doi = {https://doi.org/10.64388/IREV9I6-1712519}
  }