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Analysis of and defense against crowd-retweeting based spam in social networks

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Abstract

Social networking websites with microblogging functionality, such as Twitter or Sina Weibo, have emerged as popular platforms for discovering real-time information on the Web. Like most Internet services, these websites have become the targets of spam campaigns, which contaminate Web contents and damage user experiences. Spam campaigns have become a great threat to social network services. In this paper, we investigate crowd-retweeting spam in Sina Weibo, the counterpart of Twitter in China. We carefully analyze the characteristics of crowd-retweeting spammers in terms of their profile features, social relationships and retweeting behaviors. We find that although these spammers are likely to connect more closely than legitimate users, the underlying social connections of crowd-retweeting campaigns are different from those of other existing spam campaigns because of the unique features of retweets that are spread in a cascade. Based on these findings, we propose retweeting-aware link-based ranking algorithms to infer more suspicious accounts by using identified spammers as seeds. Our evaluation results show that our algorithms are more effective than other link-based strategies.

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Acknowledgements

This work is supported by National Natural Science Foundation of China under Grants, No. 61370208, No. 61472081, No. 61772133, No. 61402104, No. 61320106007, No. 61370207, US NSF under awards CNS-1527303 and OAC-1642124, Collaborative Innovation Center of Wireless Communications Technology, Collaborative Innovation Center of Social Safety Science and Technology, Jiangsu Provincial Key Laboratory of Network and Information Security (BM2003201),and Key Laboratory of Computer Network and Information Integration of Ministry of Education of China under Grants No. 93K-9.

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Authors and Affiliations

  1. School of Computer Science and Engineering, Southeast Univerisity, NanJing, Jiangsu, China

    Bo Liu, Zeyang Ni, Junzhou Luo, Jiuxin Cao & Xudong Ni

  2. Department of Computer Science, University of Massachusetts Lowell, Lowell, MA, USA

    Benyuan Liu

  3. Department of Computer Science, University of Central Florida, Orlando, FL, USA

    Xinwen Fu

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  1. Bo Liu
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Correspondence to Bo Liu.

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This article belongs to the Topical Collection: Special Issue on Social Computing and Big Data Applications

Guest Editors: Xiaoming Fu, Hong Huang, Gareth Tyson, Lu Zheng, and Gang Wang

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Liu, B., Ni, Z., Luo, J. et al. Analysis of and defense against crowd-retweeting based spam in social networks. World Wide Web 22, 2953–2975 (2019). https://doi.org/10.1007/s11280-018-0613-y

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  • DOI: https://doi.org/10.1007/s11280-018-0613-y

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