SocialBot: Behavioral Analysis and Detection

  • Madhuri DewanganEmail author
  • Rishabh Kaushal
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 625)


Bots refer to automated software that have the capability to execute commands on receiving instructions from BotMaster. SocialBots are the bots present in Online Social Network (OSN) which mimic the activities of the real users. They have the capability to automatically perform the basic functionalities offered by the OSN platforms. These socialbots have widespread usage in political campaigning and product marketing, but SocialBots can also been used for the purpose of swaying voters, mounting political attacks, manipulating public opinion, etc. Apart from these, SocialBots posses various security risks, one of which is befriending an OSN user thereby gaining access to personal details such as birthday, email id, phone number, address, etc. Detection of these SocialBots is therefore an important problem to be solved in order to maintain the reputation of OSN. Our work concentrates on behavioral analysis of these SocialBots in the OSN and identifying features to be used to develop a model for detection of these Socialbots using machine learning. The model, thus developed, is further used as a background process to create a web-based tool for detection of SocialBots. In our work, we created a SocialBot to perform behavioral analysis. This SocialBot got a good response from the real users and was able to grab 100+ real followers along with some real interactions in form of retweet, mention and direct messages.


Online Social Network Social engineering SocialBots Feature extraction Machine learning 


  1. 1.
    The socialbot network: when bots socialize for fame and money. In: Proceeding ACSAC 2011 Proceedings of the 27th Annual Computer Security Applications Conference, pp. 93–102. ACM, New York (2011)Google Scholar
  2. 2.
    Wagner, C., Mitter, S., Strohmaier, M., Krner, C.: When social bots attack: modeling susceptibility of users in online social networks. In: MSM (2012)Google Scholar
  3. 3.
    Boshmaf, Y., Muslukhov, I., Beznosov, K., Ripeanu, M.: Key challenges in defending against malicious socialbots. In: 5th USENIX Workshop on Large Scale Exploits and Emergent Threats LEET 2012 (2014)Google Scholar
  4. 4.
    Ferrara, E., Varol, O., Davis, C, Menczer, F., Flammini, A.: The Rise of Social BotsGoogle Scholar
  5. 5.
    Hwang, T., Pearce, I., Nanis, M.: SocialBots: voices from the fronts. Mag. Interact. 19(2), 38–45 (2012). ACM, New YorkCrossRefGoogle Scholar
  6. 6.
    Elishar, A., Fire, M., Kagan, D., Elovici, Y.: Organizational intrusion: organization mining using socialbots. In: 2012 International Conference on Social Informatics (SocialInformatics) (2012)Google Scholar
  7. 7.
    Yazan, B., Muslukhov, I., Beznosov, K., Ripeanu, M.: Design and analysis of a social botnet. Comput. Netw.: Botnet Act.: Anal. Detect. Shutdown 57(2), 556–578 (2013)Google Scholar
  8. 8.
  9. 9.
    Steps Toward Tracking and Managing Your Digital Footprint.
  10. 10.
    Facebook has more than 83 million illegitimate accounts.
  11. 11.
  12. 12.
    Boshmaf, Y., Ripeanu, M., Beznosov, K., Santos-Neto, E.: Thwarting fake OSN accounts by predicting their victims. In: Proceeding AISec 2015 Proceedings of the 8th ACM Workshop on Artificial Intelligence and Security. ACM, New York (2015)Google Scholar
  13. 13.
    Dickerson, J.P., Kagan, V., Subrahmanian, V.S.: Using Sentiment to Detect Bots on Twitter: Are Humans More Opinionated than Bots?Google Scholar
  14. 14.
  15. 15.
  16. 16.
    Gepettos Army: Creating International Incidents with Twitter Bots.
  17. 17.
    Twitter Analytics.
  18. 18.
  19. 19.
  20. 20.

Copyright information

© Springer Nature Singapore Pte Ltd. 2016

Authors and Affiliations

  1. 1.Department of Information TechnologyIndira Gandhi Delhi Technical University for WomenNew DelhiIndia

Personalised recommendations