NET-SPAM : A Network Based Spam Detection Framework For reviews in online Social Media

Authors

  • Sayyeda Zeba  Department of Computer Science Engineering, SECAB Institute of Engineering and Technology,Vijayapura Karnataka, India
  • Zarina Begam K Mundargi  Department of Computer Science Engineering, SECAB Institute of Engineering and Technology,Vijayapura Karnataka, India

Keywords:

Social Media, Social Network, Spammer, Spam Review, Fake Review, Heterogeneous Information Networks.

Abstract

Now a days, people confide on available content in social media in their decisions (e.g. reviews and feed back on a topic or product).For different interests and services, a spammers which can write spam reviews about their products that can leave a review. So far strategy used to detect spam reviews to show importance of each extracted feature type. A novel structure, named Net spam, which utilizes spam features for modeling review datasets as heterogeneous information networks to map spam detection procedure into classification problems in such networks. with the help of this features it help us to obtain better results for different experimented metrics on real-world review datasets from Amazon websites.Net Spam out performs the existing methods among four categories of features are; review-behavioral, user-behavioral, review linguistic, user-linguistic, review behavioral performs better than other categories.

References

  1. J. Donfro, A whopping 20 % of yelp reviews are fake. http://www.businessinsider.com/20-percent-of-yelp-reviews-fake-2013-9. Accessed: 2015-07-30.
  2. B. Viswanath, M. Ahmad Bashir, M. Crovella, S. Guah, K. P. Gummadi, B. Krishnamurthy, and A. Mislove. Towards detecting anomalous user behavior in online social networks. In USENIX, 2014.
  3. A. Mukherjee, A. Kumar, B. Liu, J. Wang, M. Hsu, M. Castellanos, and R. Ghosh. Spotting opinion spammers using behavioral footprints. In ACM KDD, 2013.
  4. F. Li, M. Huang, Y. Yang, and X. Zhu. Learning to identify review spam. Proceedings of the 22nd International Joint Conference on Artificial Intelligence; IJCAI, 2011.
  5. G. Fei, A. Mukherjee, B. Liu, M. Hsu, M. Castellanos, and R. Ghosh. Exploiting burstiness in reviews for review spammer detection. In ICWSM, 2013.
  6. A. Mukerjee, V. Venkataraman, B. Liu, and N. Glance. What Yelp Fake Review Filter Might Be Doing?, In ICWSM, 2013.
  7. L. Akoglu, R. Chandy, and C. Faloutsos. Opinion fraud detection in online reviews bynetwork effects. In ICWSM, 2013.
  8. Survey on "Combating product review spam campaigns via multiple heterogeneous pairwise features" (Ch. Xu and J. Zhang)
  9. "Towards detecting anomalous userbehavior in online social networks".( B. Viswanath, M. Ahmad Bashir, M. Crovella, S. Guah, K. P. Gummadi, B. Krishnamurthy, and A. Mislove.)
  10. "Collective opinion spam detection: bridging review networks and metadata". (R. Shebuti and L. Akoglu.)
  11. "Trust-Aware Review SpamDetectio"(H. Xue, F. Li, H. Seo, and R. Pluretti.)11]
  12. R. Shebuti and L. Akoglu. Collective opinion spam detection: bridging review networksand metadata. In ACM KDD, 2015.

IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2018-04-30

Issue

Volume 3, Issue 3, March-April-2018

Section

Research Articles

License

Copyright (c) IJSRCSEIT

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Sayyeda Zeba, Zarina Begam K Mundargi, " NET-SPAM : A Network Based Spam Detection Framework For reviews in online Social Media, IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 3, Issue 3, pp.2053-2057, March-April-2018.