Abstract
Social bots are intelligent programs that have the ability to receive instructions and mimic real users’ behaviors on social networks, which threaten social network users’ information security. Current researches focus on modeling classifiers from features of user profile and behaviors that could not effectively detect burgeoning social bots. This paper proposed to detect social bots on Twitter based on tweets similarity which including content similarity, tweet length similarity, punctuation usage similarity and stop words similarity. In addition, the LSA (Latent semantic analysis) model is adopted to calculate similarity degree of content. The results show that tweets similarity has significant effect on social bot detection and the proposed method can reach 98.09% precision rate on new data set, which outperforms Madhuri Dewangan’s method.
Project supported by National Key R&D Program of China (2017YFB0802703), National Natural Science Foundation of China (61602052).
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© 2018 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Wang, Y., Wu, C., Zheng, K., Wang, X. (2018). Social Bot Detection Using Tweets Similarity. In: Beyah, R., Chang, B., Li, Y., Zhu, S. (eds) Security and Privacy in Communication Networks. SecureComm 2018. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 255. Springer, Cham. https://doi.org/10.1007/978-3-030-01704-0_4
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