Abstract
Millions of users generate and propagate information in online social network. Search engines and data mining tools allow people to track hot topics and events online. However, the massive use of social media also makes it easier for malicious users, known as social spammers, to occupy social network with junk information. To solve this problem, a classifier is needed to detect social spammers. One effective way for spammer detection is based on contents and user information. Nevertheless, social spammers are tricky and able to fool the system with evolving their contents and information. Firstly, social spammers continually change their patterns to deceive detecting system. Secondly, spammers will try to gain influence and disguise themselves as far as possible. Due to the dynamic pattern of social spammers, it is difficult for existing methods to effectively and efficiently respond to social spammers. In this paper, we present a model based on logistic regression considering content attributes and behavior attributes of users in social network. Analyses of user attributes are made to differentiate spammers and non-spammers inherently. Experimental results on Twitter data show the effectiveness and efficiency of the proposed method.
X. Zhu—Sponsored by National Key fundamental Research and Development Program No. 2013CB329601.
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Zhu, X., Nie, Y., Jin, S., Li, A., Jia, Y. (2015). Spammer Detection on Online Social Networks Based on Logistic Regression. In: Xiao, X., Zhang, Z. (eds) Web-Age Information Management. WAIM 2015. Lecture Notes in Computer Science(), vol 9391. Springer, Cham. https://doi.org/10.1007/978-3-319-23531-8_3
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DOI: https://doi.org/10.1007/978-3-319-23531-8_3
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