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Social Bot Detection Using Tweets Similarity

  • Yahan Wang
  • Chunhua WuEmail author
  • Kangfeng Zheng
  • Xiujuan Wang
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 255)

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.

Keywords

Social bot LSA Tweets similarity Machine learning 

References

  1. 1.
    Charikar, M.S.: Similarity estimation techniques from rounding algorithms. In: Proceedings of ACM Symposium on Theory of Computing, STOC, pp. 380–388 (2002)Google Scholar
  2. 2.
    Chavoshi, N., Hamooni, H., Mueen, A.: Identifying correlated bots in Twitter. In: International Conference on Social Informatics, pp. 14–21 (2016)Google Scholar
  3. 3.
    Chu, Z., Gianvecchio, S., Wang, H., Jajodia, S.: Who is tweeting on Twitter: human, bot, or cyborg? In: Computer Security Applications Conference, pp. 21–30 (2010)Google Scholar
  4. 4.
    Clayton A. Davis, Onur Varol, E.F.: Bot or not? http://truthy.indiana.edu/botornot/
  5. 5.
    Dewangan, M., Kaushal, R.: SocialBot: Behavioral Analysis and Detection. Springer, Singapore (2016).  https://doi.org/10.1007/978-981-10-2738-3_39CrossRefGoogle Scholar
  6. 6.
    Dickerson, J.P., Kagan, V., Subrahmanian, V.S.: Using sentiment to detect bots on Twitter: are humans more opinionated than bots? In: IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 620–627 (2014)Google Scholar
  7. 7.
    Dumais, S.T.: Latent semantic analysis. Ann. Rev. Inf. Sci. Technol. 38(1), 188–230 (2015)CrossRefGoogle Scholar
  8. 8.
    Evangelopoulos, N.E.: Latent semantic analysis. Wiley Interdisc. Rev. Cogn. Sci. 4(6), 683–692 (2013)CrossRefGoogle Scholar
  9. 9.
    Glvez, R.H., Gravano, A.: Assessing the usefulness of online message board mining in automatic stock prediction systems. J. Comput. Sci. 19, 43–56 (2017)CrossRefGoogle Scholar
  10. 10.
    Golder, S.A., Macy, M.W.: Diurnal and seasonal mood vary with work, sleep, and daylength across diverse cultures. Science 333(6051), 1878–1881 (2011)CrossRefGoogle Scholar
  11. 11.
  12. 12.
    Lee, K., Eoff, B.D., Caverlee, J.: Seven months with the devils: a long-term study of content polluters on Twitter. In: International Conference on Weblogs and Social Media, Barcelona, July 2011Google Scholar
  13. 13.
  14. 14.
    Morstatter, F., Wu, L., Nazer, T.H., Carley, K.M., Liu, H.: A new approach to bot detection: striking the balance between precision and recall. In: IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 533–540 (2016)Google Scholar
  15. 15.
    Perdana, R.S., Muliawati, T.H., Alexandro, R.: Bot spammer detection in Twitter using tweet similarity and time interval entropy. J. Inorgan. Biochem. 105(4), 518–524 (2015)Google Scholar
  16. 16.
    Roesslein, J.: Tweepy. www.tweepy.org (2009)
  17. 17.
    Shafahi, M., Kempers, L., Afsarmanesh, H.: Phishing through social bots on Twitter. In: IEEE International Conference on Big Data, pp. 3703–3712 (2017)Google Scholar
  18. 18.
    Sharma, R.: 15 awesome Twitter bots you should follow (2016). https://beebom.com/best-twitter-bots/
  19. 19.
    Subrahmanian, V.S.: The darpa Twitter bot challenge. Computer 49(6), 38–46 (2016)CrossRefGoogle Scholar
  20. 20.
    U.S. Securities, E.C.: Amendment no. 1 to form s-1 (2014). http://www.sec.gov/Archives/edgar/data/1418091/000119312513400028/
  21. 21.
    Varol, O., Ferrara, E., Davis, C.A., Menczer, F., Flammini, A.: Online human-bot interactions: detection, estimation, and characterization. In: The 11th International AAAI Conference on Web and Social Media (2017)Google Scholar

Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2018

Authors and Affiliations

  • Yahan Wang
    • 1
  • Chunhua Wu
    • 1
    Email author
  • Kangfeng Zheng
    • 1
  • Xiujuan Wang
    • 2
  1. 1.Beijing University of Posts and TelecommunicationsBeijingChina
  2. 2.Beijing University of TechnologyBeijingChina

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