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Detecting cooperative and organized spammer groups in micro-blogging community

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Abstract

In recent years, social spammers become rampant and evolve a number of variations in most social networks. In micro-blogging community, there are a typical type of anomalous groups consisting of cooperative and organized spammers, and they are hired by public relation companies and paid for posting tweets with certain content. They intentionally evolve their content and behavior patterns to prevent them from being detected, and cooperatively hijack the trending topics with a deliberate point of view which would affect people’s judgments and decisions seriously. Due to the evolving nature and hidden behavior of this type of spammers, we have to deal with two important issues to solve the problem of detecting this type of spammer groups. One is to detect the anomalous topics hijacked by spammer groups from numerous trending topics. Another is to detect the members of spammer group from the users joining anomalous topics. In this paper, we propose a two-stage topology-based method to detect spammer groups partially distributed in multiple trending topics. In the first stage, we detect the anomalous topics from plenty of trending topics according to a new similarity measure based on subgraph ranking. A topic is identified as anomalous if the topology characteristics of retweeting networks between adjacent periods change dramatically. In the second stage, we obtain several anomalous topic sequences through a few initial labeled spammers by employing the basic idea of label propagation, and cluster the users who join each topic sequence into group spammers and normal users by their total authorities. The total authority of user is his/her weighted cumulative authorities in anomalous topics of each topic sequence, and authority in each topic is defined based on the out-degree of user in the retweeting network. The experimental results based on real-world data collected from Sina micro-blogging site demonstrate that our similarity measure keeps a leading performance in all evaluation metrics, and our method can effectively detect the group spammers compared with other methods.

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Notes

  1. http://www.chinadaily.com.cn/china/2010-06/17/content_9981056.htm.

  2. https://www.technologyreview.com/s/426174/undercover-researchers-expose-chinese-internet-water-army/.

  3. http://open.weibo.com/.

  4. http://weibo.com/hottopic.

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Acknowledgements

The research presented in this paper is supported in part by the National Natural Science Foundation (61572397, 61502383, 61375040, 61571360), Fundamental Research Project of Natural Science in Shaanxi Province (2015JM6298, 2015JM6299) and Specialized Research Plan Funded Project of Shaanxi Province Department of Education (15JK1505).

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Authors and Affiliations

  1. Ministry of Education Key Lab for Intelligent Networks and Network Security, Xi’an Jiaotong University, Xi’an, People’s Republic of China

    Qi Dang & Yadong Zhou

  2. Institute of Systems Engineering, Xi’an Jiaotong University, Xi’an, People’s Republic of China

    Feng Gao

  3. Xi’an University of Technology, Xi’an, People’s Republic of China

    Qindong Sun

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  1. Qi Dang
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  2. Yadong Zhou
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  3. Feng Gao
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  4. Qindong Sun
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Correspondence to Feng Gao.

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Responsible editor: G. Karypis.

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Dang, Q., Zhou, Y., Gao, F. et al. Detecting cooperative and organized spammer groups in micro-blogging community. Data Min Knowl Disc 31, 573–605 (2017). https://doi.org/10.1007/s10618-016-0479-5

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  • DOI: https://doi.org/10.1007/s10618-016-0479-5

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