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.
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References
Akoglu, L., Chandy, R., Faloutsos, C.: Opinion fraud detection in online reviews by network effects. ICWSM 13, 2–11 (2013)
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)
Cambria, E., Schuller, B., Xia, Y., Havasi, C.: New avenues in opinion mining and sentiment analysis. IEEE Intell. Syst. 28(2), 15–21 (2013)
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)
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)
Mostafa, M.M.: More than words: social networks’ text mining for consumer brand sentiments. Expert Syst. Appl. 40(10), 4241–4251 (2013)
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)
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)
Nguyen, T.H., Shirai, K., Velcin, J.: Sentiment analysis on social media for stock movement prediction. Expert Syst. Appl. 42(24), 9603–9611 (2015)
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)
Savage, D., Zhang, X., Yu, X., Chou, P., Wang, Q.: Anomaly detection in online social networks. Soc. Netw. 39, 62–70 (2014)
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)
Viswanath, B., et al.: Towards detecting anomalous user behavior in online social networks. In: USENIX Security Symposium, pp. 223–238 (2014)
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)
Wu, L., Hu, X., Morstatter, F., Liu, H.: Adaptive spammer detection with sparse group modeling. In: ICWSM, pp. 319–326 (2017)
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)
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)
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)
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Alallaq, N., Al-khiza’ay, M., Dohan, M.I., Han, X. (2018). Sentiment Analysis to Enhance Detection of Latent Astroturfing Groups in Online Social Networks. In: Chen, Q., Wu, J., Zhang, S., Yuan, C., Batten, L., Li, G. (eds) Applications and Techniques in Information Security. ATIS 2018. Communications in Computer and Information Science, vol 950. Springer, Singapore. https://doi.org/10.1007/978-981-13-2907-4_7
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DOI: https://doi.org/10.1007/978-981-13-2907-4_7
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