Abstract
Online social networks are popular communication tools for billions of users. Unfortunately, they are also effective tools for hidden paid posters (or Internet water army in some literatures) to propagate spam or mendacious messages. Paid posters are typically organized in groups to post with specific purposes and have flooded the communities of microblogging websites. Typical traditional methods only utilize individual characteristics in detecting them. In this paper, we study the group characteristics of paid posters and find that group characteristics are also very important in detecting them comparing to individual characteristics. We construct a classifier based on both the individual and group characteristics to detect paid posters. Extensive experiments show that our method is better than existing methods.
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
Similar content being viewed by others
Notes
- 1.
- 2.
MapReduce: http://en.wikipedia.org/wiki/MapReduce.
- 3.
Apache Hadoop: http://en.wikipedia.org/wiki/Apache_Hadoop.
- 4.
ICTCLAS: http://ictclas.org/index.html.
- 5.
SINA Weibo: http://www.weibo.com/.
References
Sina weibo. http://en.wikipedia.org/wiki/sina_weibo, June 2014
Androutsopoulos, I., Koutsias, J., Chandrinos, K.V., Spyropoulos, C.D.: An experimental comparison of naive Bayesian and keyword-based anti-spam filtering with personal e-mail messages. In: Proceedings of the 23rd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 160–167. ACM (2000)
Benevenuto, F., Duarte, F., Rodrigues, T., Almeida, V.A., Almeida, J.M., Ross, K.W.: Understanding video interactions in youtube. In: Proceedings of the 16th ACM international conference on Multimedia, pp. 761–764. ACM (2008)
Benevenuto, F., Magno, G., Rodrigues, T., Almeida, V.: Detecting spammers on twitter. In: Collaboration, Electronic Messaging, Anti-Abuse and Spam Conference (CEAS), vol. 6, p. 12 (2010)
Benevenuto, F., Rodrigues, T., Almeida, V., Almeida, J., Zhang, C., Ross, K.: Identifying video spammers in online social networks. In: Proceedings of the 4th International Workshop on Adversarial Information Retrieval on the Web, pp. 45–52. ACM (2008)
Blanzieri, E., Bryl, A.: A survey of learning-based techniques of email spam filtering. Artif. Intell. Rev. 29(1), 63–92 (2008)
Chang, C.-C., Lin, C.-J.: LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol. 2(3), 27:1–27:27 (2011)
Chen, C., Wu, K., Srinivasan, V., Zhang, X.: Battling the internet water army: detection of hidden paid posters. In: Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 116–120. ACM (2013)
Chu, Z., Gianvecchio, S., Wang, H., Jajodia, S.: Who is tweeting on twitter: human, bot, or cyborg? In: Proceedings of the 26th Annual Computer Security Applications Conference, pp. 21–30. ACM (2010)
Ding, Z., Jia, Y., Zhou, B., Han, Y.: Mining topical influencers based on the multi-relational network in micro-blogging sites. China Commun. 10(1), 93–104 (2013)
Drucker, H., Wu, S., Vapnik, V.N.: Support vector machines for spam categorization. IEEE Trans. Neural Netw. 10(5), 1048–1054 (1999)
Fetterly, D., Manasse, M., Najork, M.: Spam, damn spam, and statistics: using statistical analysis to locate spam web pages. In: Proceedings of the 7th International Workshop on the Web and Databases: Colocated with ACM SIGMOD/PODS 2004, pp. 1–6. ACM (2004)
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 (2010)
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 (2010)
Gyöngyi, Z., Garcia-Molina, H., Pedersen, J.: Combating web spam with trustrank. In: Proceedings of the Thirtieth International Conference on Very Large Data Bases, VLDB Endowment, vol. 30, pp. 576–587 (2004)
**dal, N., Liu, B.: Opinion spam and analysis. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 219–230. ACM (2008)
Kolari, P., Java, A., Finin, T., Oates, T., Joshi, A.: Detecting spam blogs: a machine learning approach. In: Proceedings of the National Conference on Artificial Intelligence, vol. 21, pp. 1351. AAAI Press, Menlo Park, CA (1999), MIT Press, Cambridge, London, MA (2006)
Lee, K., Caverlee, J., Webb, S.: Uncovering social spammers: social honeypots+ machine learning. In: Proceedings of the 33rd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 435–442. ACM (2010)
McCord, M., Chuah, M.: Spam detection on twitter using traditional classifiers. In: Alcaraz Calero, J.M., Yang, L.T., Mármol, F.G., Villalba, L.J.G., Li, A.X., Wang, Y. (eds.) ATC 2011. LNCS, vol. 6906, pp. 175–186. Springer, Heidelberg (2011)
Ott, M., Choi, Y., Cardie, C., Hancock, J.T.: Finding deceptive opinion spam by any stretch of the imagination. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, vol. 1, pp. 309–319. Association for Computational Linguistics (2011)
Salton, G., Wong, A., Yang, C.-S.: A vector space model for automatic indexing. Commun. ACM 18(11), 613–620 (1975)
Sebastiani, F.: Machine learning in automated text categorization. ACM Comput. Surv. 34(1), 1–47 (2002)
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 (2011)
Thomason, A.: Blog spam: a review. In: CEAS (2007)
Wang, K., **ao, Y., **ao, Z.: Detection of internet water army in social network. In: 2014 International Conference on Computer, Communications and Information Technology (CCIT 2014). Atlantis Press (2014)
Zhang, Y., Ruan, X., Wang, H., Wang, H.: What scale of audience a campaign can reach in what price. In: 2014 IEEE International Conference on Computer Communications (InfoCOM 2014) (2014)
Yang, C., Harkreader, R., Zhang, J., Shin, S., Gu,G.: Analyzing spammers’ social networks for fun and profit: a case study of cyber criminal ecosystem on twitter. In: Proceedings of the 21st International Conference on World Wide Web, pp. 71–80. ACM (2012)
Zeng, K., Wang, X., Zhang, Q., Zhang, X., Wang, F.-Y.: Behavior modeling of internet water army in online forums. World Congr. 19, 9858–9863 (2014)
Acknowledgments
This work was supported by 973 Program of China (Grant No. 2013CB329601, 2013CB329602, 2013CB329604), NSFC of China (Grant No. 60933005, 91124002), 863 Program of China (Grant No. 2012AA01A401, 2012AA01A402), National Key Technology RD Program of China (Grant No. 2012BAH38B04, 2012BAH38B06).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Wang, X., Zhou, B., Jia, Y., Li, S. (2015). Detecting Internet Hidden Paid Posters Based on Group and Individual Characteristics. In: Wang, J., et al. Web Information Systems Engineering – WISE 2015. WISE 2015. Lecture Notes in Computer Science(), vol 9419. Springer, Cham. https://doi.org/10.1007/978-3-319-26187-4_8
Download citation
DOI: https://doi.org/10.1007/978-3-319-26187-4_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-26186-7
Online ISBN: 978-3-319-26187-4
eBook Packages: Computer ScienceComputer Science (R0)