Abstract
This paper describes an approach to identify suspected cybermob on social media. Many researches involve making predictions of group emotion on Internet (such as quantifying sentiment polarity), but this paper instead focuses on the origin of information diffusion, namely back to its makers and contributors. According our previous findings that have shown, at the level of Tieba’s contents, the negative information or emotions spread faster than positive ones, we centre on the maker of negative message in this paper, so-called cybermobs who post aggressive, provocative or insulting remarks on social websites. We explore the different characteristics between suspected cybermobs and general netizens and then extract relative unique features of suspected cybermobs. We construct real system to identify suspected cybermob automatically using machine learning method with above features, including other common features like user/content-based ones. Empirical results show that our approach can detect suspected cybermob correctly and efficiently as we evaluate it with benchmark models, and apply it to actual cases.
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
References
CNNIC: 36th China Internet Development Statistics Report. China Internet Network Information Center (2015)
Sun, W.: Analysis of the causes of “cyber violence”. Perspective of Communication Psychology. People Daily (2008)
Jun-Xiang, L.I.: Reflections on violence of public opinion in modern media environment: analysis of the kneeling incident of Li Yang’s students. J. Maoming Univ. 2, 014 (2008)
Tao, S.: “Cybermob” with its legal regulation. Theor. Res. 109 (2014)
Liu, X.: Analysis on infringement speech of cybermob. People Daily. China Dominant-Journalism Development Center (2007)
Hou, Z.: Reconstruction of the sense of responsibility to resolve the cyber violence. Democracy Legal Syst. 10 (2006)
Cao, N.: Young children defecation in “Hong Kong” the bedizen comments and events “internet mob” phenomenon analysis. Inner Mongolia University (2015)
Qian, M., Ke, Y.: Overview of Web Spammer Detection. J. Softw. (2014)
Lau, R.Y.K., Liao, S.Y., Kwok, C.W., Xu, K., Xia, Y., Li, Y.: Text mining and probabilistic language modeling for online review spam detection. ACM Trans. Manag. Inf. Syst. 2, 1 (2011)
Liu, H., Zhao, Y., Qin, B., Liu, T.: Comment target extraction and sentiment classification. J. Chin. Inf. Process. 24(1), 84–88 (2010)
Jindal, N., Liu, B., Lim, E.P.: Finding unusual review patterns using unexpected rules. In: ACM Conference on Information and Knowledge Management, CIKM 2010, Toronto, Ontario, Canada, October, vol. 1549 (2010)
Benevenuto, F., Magno, G., Rodrigues, T., Almeida, V.: Detecting spammers on Twitter (2013)
Zhu, Y., Wang, X., Zhong, E., Liu, N.N., Li, H., Yang, Q.: Discovering spammers in social networks. In: AAAI Conference on Artificial Intelligence (2012)
Hao, S., Syed, N.A., Feamster, N., Gray, A.G., Krasser, S.: Detecting spammers with SNARE: spatio-temporal network-level automatic reputation engine. In: Proceedings of the Usenix Security Symposium, Montreal, Canada, 10–14 August 2009, vol. 101 (2009)
Kincaird, J.: Edgerank: the secret sauce that makes Facebook’s news feed tick. TechCrunch, April 2010
Amleshwaram, A.A., Reddy, N., Yadav, S., Gu, G., Yang, C.: CATS: characterizing automation of Twitter spammers. In: Fifth International Conference on Communication Systems and Networks, vol. 1 (2013)
Lin, C., He, J., Zhou, Y., Yang, X., Chen, K., Song, L.: Analysis and identification of spamming behaviors in Sina Weibo microblog. In: The Workshop on Social Network Mining & Analysis, vol. 1 (2013)
Zheng, X., Zeng, Z., Chen, Z., Yu, Y., Rong, C.: Detecting spammers on social networks. Neurocomputing 42, 27 (2015)
Morris, M.R., Counts, S., Roseway, A., Hoff, A., Schwarz, J.: Tweeting is believing?: Understanding microblog credibility perceptions. In: Proceedings, p. 441 (2012)
Yang, F., Liu, Y., Yu, X., Yang, M.: Automatic detection of rumor on Sina Weibo. In: ACM SIGKDD Workshop on Mining Data Semantics, vol. 1 (2012)
Yang, J., Counts, S., Morris, M.R., Hoff, A.: Microblog credibility perceptions: comparing the USA and China. In: Conference on Computer Supported Cooperative Work, vol. 575 (2013)
Li, R., Wang, J., Chen, X., Tao, X., Hu, Y.: Using maximum entropy model for Chinese text categorization. J. Comput. Res. Dev. 42, 578 (2005)
Acknowledgments
We thank reviewers for their constructive comments, and gratefully acknowledge the support of Natural Science Foundation of China (61201352) and the Major State Basic Research Development Program (973 Program) of China (2013CB329606).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing AG
About this paper
Cite this paper
Shi, S., Zhou, X., Zhao, M., Huang, H. (2016). Identifying Suspected Cybermob on Tieba. In: Sun, M., Huang, X., Lin, H., Liu, Z., Liu, Y. (eds) Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data. NLP-NABD CCL 2016 2016. Lecture Notes in Computer Science(), vol 10035. Springer, Cham. https://doi.org/10.1007/978-3-319-47674-2_31
Download citation
DOI: https://doi.org/10.1007/978-3-319-47674-2_31
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-47673-5
Online ISBN: 978-3-319-47674-2
eBook Packages: Computer ScienceComputer Science (R0)