Advertisement

Sentiment Analysis to Enhance Detection of Latent Astroturfing Groups in Online Social Networks

  • Noora AlallaqEmail author
  • Muhmmad Al-khiza’ayEmail author
  • Mohammed Iqbal Dohan
  • Xin Han
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 950)

Abstract

Astroturfing is a sponsored and organized campaign for making a favor to the sponsor by the act of expressing an opinion via reviews on a product or service. Such reviews are misleading and cause potential risk to the public in general as there has been increasing trend of making decisions based on the online reviews. In this paper, we proposed an enhanced LDA to have a novel model known as Latent Group Detective Model (LGDM) which is meant for solving the problem of astroturfing group detection. This model is based on the fact that there is a difference between a generative process of normal review and a potential astroturfing review. LDGM accommodate a label for finding sentiment associated with reviews to have the better analysis of AGs. Four cases are considered for AG analysis. Case 1 observes the groups as they are discovered. Case 2 performs opposite sentiment analysis. Case 3 focuses on high sentiment reviews while case 4 deals with change of sentiment of potential AGs across products or services.

Keywords

Online reviews Astroturfing Astroturfing group LGDM Sentiment analysis 

References

  1. 1.
    Akoglu, L., Chandy, R., Faloutsos, C.: Opinion fraud detection in online reviews by network effects. ICWSM 13, 2–11 (2013)Google Scholar
  2. 2.
    Akoglu, L., Faloutsos, C.: Anomaly, event, and fraud detection in large network datasets. In: Proceedings of the Sixth ACM International Conference on Web Search and Data Mining, pp. 773–774. ACM (2013)Google Scholar
  3. 3.
    Cambria, E., Schuller, B., Xia, Y., Havasi, C.: New avenues in opinion mining and sentiment analysis. IEEE Intell. Syst. 28(2), 15–21 (2013)CrossRefGoogle Scholar
  4. 4.
    He, W., Zha, S., Li, L.: Social media competitive analysis and text mining: a case study in the pizza industry. Int. J. Inf. Manag. 33(3), 464–472 (2013)CrossRefGoogle Scholar
  5. 5.
    Hooi, B., et al.: BIRDNEST: Bayesian inference for ratings-fraud detection. In: Proceedings of the 2016 SIAM International Conference on Data Mining, pp. 495–503. SIAM (2016)Google Scholar
  6. 6.
    Mostafa, M.M.: More than words: social networks’ text mining for consumer brand sentiments. Expert Syst. Appl. 40(10), 4241–4251 (2013)CrossRefGoogle Scholar
  7. 7.
    Mukherjee, A., Liu, B., Glance, N.: Spotting fake reviewer groups in consumer reviews. In: Proceedings of the 21st International Conference on World Wide Web, pp. 191–200. ACM (2012)Google Scholar
  8. 8.
    Mukherjee, A., Venkataraman, V., Liu, B., Glance, N.: Fake review detection: classification and analysis of real and pseudo reviews. Technical report, UIC-CS-2013-03, University of Illinois at Chicago (2013)Google Scholar
  9. 9.
    Nguyen, T.H., Shirai, K., Velcin, J.: Sentiment analysis on social media for stock movement prediction. Expert Syst. Appl. 42(24), 9603–9611 (2015)CrossRefGoogle Scholar
  10. 10.
    Rayana, S., Akoglu, L.: Collective opinion spam detection: bridging review networks and metadata. In: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 985–994. ACM (2015)Google Scholar
  11. 11.
    Savage, D., Zhang, X., Yu, X., Chou, P., Wang, Q.: Anomaly detection in online social networks. Soc. Netw. 39, 62–70 (2014)CrossRefGoogle Scholar
  12. 12.
    Song, J., Lee, S., Kim, J.: CrowdTarget: target-based detection of crowdturfing in online social networks. In Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security, pp. 793–804. ACM (2015)Google Scholar
  13. 13.
    Viswanath, B., et al.: Towards detecting anomalous user behavior in online social networks. In: USENIX Security Symposium, pp. 223–238 (2014)Google Scholar
  14. 14.
    Wang, H., Can, D., Kazemzadeh, A., Bar, F., Narayanan, S.: A system for real-time twitter sentiment analysis of 2012 us presidential election cycle. In: Proceedings of the ACL 2012 System Demonstrations, pp. 115–120. Association for Computational Linguistics (2012)Google Scholar
  15. 15.
    Wu, L., Hu, X., Morstatter, F., Liu, H.: Adaptive spammer detection with sparse group modeling. In: ICWSM, pp. 319–326 (2017)Google Scholar
  16. 16.
    Xiang, Z., Du, Q., Ma, Y., Fan, W.: A comparative analysis of major online review platforms: implications for social media analytics in hospitality and tourism. Tour. Manag. 58, 51–65 (2017)CrossRefGoogle Scholar
  17. 17.
    Xiao, C., Freeman, D.M., Hwa, T.: Detecting clusters of fake accounts in online social networks. In: Proceedings of the 8th ACM Workshop on Artificial Intelligence and Security, pp. 91–101. ACM (2015)Google Scholar
  18. 18.
    Zhao, J., Dong, L., Wu, J., Xu, K.: MoodLens: an emoticon-based sentiment analysis system for Chinese tweets. In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1528–1531. ACM (2012)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.School of Information TechnologyDeakin UniversityBurwoodAustralia
  2. 2.Department of Multimedia, College of Computer Science and Information TechnologyUniversity of Al-QadisiyahDiwaniyahIraq
  3. 3.College of Computer ScienceXi’an Shiyou UniversityXi’anChina

Personalised recommendations