Journal
WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS
Volume 22, Issue 6, Pages 2953-2975Publisher
SPRINGER
DOI: 10.1007/s11280-018-0613-y
Keywords
Social network; Crowd-retweeting; Spamming and microblogging
Funding
- National Natural Science Foundation of China [61370208, 61472081, 61772133, 61402104, 61320106007, 61370207]
- US NSF [CNS-1527303, 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]
- Key Laboratory of Computer Network and Information Integration of Ministry of Education of China [93K-9]
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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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