Abstract
Online social networks such as Twitter have become a major type of information sources in recent years. However, this new public social media provides new gateways for malicious users to achieve various malicious purposes. In this paper, we introduce an extended trust model for detecting malicious activities in online social networks. The major insight is to conduct a trust propagation process over a novel heterogeneous social graph which is able to model different social activities. We develop two trustworthiness measures and evaluate their performance of detecting malicious activities using a real Twitter data set. The results revealed that the F-1 measure of detecting malicious activities in Twitter can achieve higher than 0.9 using our proposed method.
A preliminary version of this work was presented as a poster at the 2013 IEEE/WIC/ACM International Conference on Web Intelligence (WI 2013).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Alfarez Abdul-Rahman, S.H.: A distributed trust model (1997)
Benevenuto, F., Rodrigues, T., Cha, M., Almeida, V.: Characterizing user behavior in online social networks. In: Proceedings of the 9th ACM SIGCOMM Conference on Internet Measurement Conference, pp. 49–62. ACM, New York (2009)
Cormack, G.V.: Email spam filtering: A systematic review. Foundations and Trends in Information Retrieval 1(4), 335–455 (2008)
Duric, A., Song, F.: Feature selection for sentiment analysis based on content and syntax models. In: Proceedings of the 2nd Workshop on Computational Approaches to Subjectivity and Sentiment Analysis, Stroudsburg, USA, pp. 96–103 (2011)
Gao, H., Chen, Y., Lee, K., Palsetia, D., Choudhary, A.: Poster: online spam filtering in social networks. In: Proceedings of the 18th ACM Conference on Computer and Communications Security, pp. 769–772. ACM, New York (2011)
Gao, H., Hu, J., Wilson, C., Li, Z., Chen, Y., Zhao, B.Y.: Detecting and characterizing social spam campaigns. In: Proceedings of the 10th ACM SIGCOMM Conference on Internet Measurement, pp. 35–47. ACM, New York (2010)
Grant, R.: Social media around the world 2012 (October 2012)
Grier, C., Thomas, K., Paxson, V., Zhang, M.: @spam: the underground on 140 characters or less. In: Proceedings of the 17th ACM Conference on Computer and Communications Security, pp. 27–37. ACM, New York (2010)
Hassan, A., Qazvinian, V., Radev, D.: What’s with the attitude?: identifying sentences with attitude in online discussions. In: Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing, Stroudsburg, USA, pp. 1245–1255 (2010)
Haveliwala, T.: Topic-sensitive pagerank. In: Proceedings of the 11st International World Wide Web Conference (WWW 2002), Honolulu, Hawaii, pp. 784–796. ACM (2002)
Heymann, P., Koutrika, G., Garcia-Molina, H.: Fighting spam on social web sites: A survey of approaches and future challenges 11(6), 36–45 (2007)
Irani, D., Webb, S., Pu, C.: Study of static classification of social spam profiles in myspace. In: Proceedings of the Fourth International Conference on Weblogs and Social Media (ICWSM 2010). The AAAI Press (2010)
Jin, X., Lin, C.X., Luo, J., Han, J.: Socialspamguard: A data mining-based spam detection system for social media networks. PVLDB 4(12), 1458–1461 (2011)
Lee, K., Caverlee, J., Webb, S.: The social honeypot project: protecting online communities from spammers. In: Proceedings of the 19th International Conference on World Wide Web, WWW 2010, pp. 1139–1140. ACM, New York (2010)
Qazvinian, V., Rosengren, E., Radev, D.R., Mei, Q.: Rumor has it: identifying misinformation in microblogs. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, Stroudsburg, USA, pp. 1589–1599 (2011)
Ratkiewicz, J., Conover, M., Meiss, M., Gonçalves, B., Patil, S., Flammini, A., Menczer, F.: Truthy: mapping the spread of astroturf in microblog streams. In: Proceedings of the 20th International Conference Companion on World Wide Web, pp. 249–252. ACM, New York (2011)
Stringhini, G., Kruegel, C., Vigna, G.: Detecting spammers on social networks. In: Annual Computer Security Applications Conference (2010)
Thomas, K., Grier, C., Song, D., Paxson, V.: Suspended accounts in retrospect: an analysis of twitter spam. In: Proceedings of the 2011 ACM SIGCOMM Conference on Internet Measurement Conference, pp. 243–258. ACM, New York (2011)
http://support.twitter.com/articles/64986-how-to-report-spam-on-twitter
Xie, Y., Yu, F., Achan, K., Panigrahy, R., Hulten, G., Osipkov, I.: Spamming botnets: signatures and characteristics. In: Proceedings of the ACM SIGCOMM 2008 Conference on Data Communication, pp. 171–182. ACM, New York (2008)
Yang, F., Liu, Y., Yu, X., Yang, M.: Automatic detection of rumor on sina weibo. In: Proceedings of the ACM SIGKDD Workshop on Mining Data Semantics, pp. 13:1–13:7. ACM, New York (2012)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Agarwal, M., Zhou, B. (2014). Using Trust Model for Detecting Malicious Activities in Twitter. In: Kennedy, W.G., Agarwal, N., Yang, S.J. (eds) Social Computing, Behavioral-Cultural Modeling and Prediction. SBP 2014. Lecture Notes in Computer Science, vol 8393. Springer, Cham. https://doi.org/10.1007/978-3-319-05579-4_25
Download citation
DOI: https://doi.org/10.1007/978-3-319-05579-4_25
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-05578-7
Online ISBN: 978-3-319-05579-4
eBook Packages: Computer ScienceComputer Science (R0)