ABSTRACT
Twitter bots have evolved from easily-detectable, simple content spammers with bogus identities to sophisticated players embedded in deep levels of social networks, silently promoting affiliate campaigns, marketing various products and services, and orchestrating or coordinating political activities. Much research has been reported on building accurate machine learning classifiers to identifying bots in social networks; recent works on social bots have started the new line of research on the existence, placement, and functions of the bots in a collective manner. In this paper, we study two families of Twitter bots which have been studied previously with respect to spamming activities through advertisement and political campaigns, and perform an evolutionary comparison with the new waves of bots currently found in Twitter. We uncover various evolved tendencies of the new social bots under social, communication, and behavioral patterns. Our findings show that these bots demonstrate evolved core-periphery structure; are deeply embedded in their networks of communication; exhibit complex information diffusion and heterogeneous content authoring patterns; perform mobilization of leaders across communication roles; and reside in niche topic communities. These characteristics make them highly deceptive as well as more effective in achieving operational goals than their traditional counterparts. We conclude by discussing some possible applications of the discovered behavioral and social traits of the evolved bots, and ways to build effective bot detection systems.
- K. Lee, B. D. Eoff, and J. Caverlee, "Seven months with the devils: A long-term study of content polluters on twitter," In Fifth International AAAI Conference on Weblogs and Social Media, July 2011.Google Scholar
- A. A. Amleshwaram, N. Reddy, S. Yadav, G. Gu, and C. Yang, "Cats: Characterizing automation of twitter spammers," In 2013 Fifth International Conference on Communication Systems and Networks (COMSNETS), pp. 1--10, IEEE, January 2013.Google Scholar
- S. Ghosh, B. Viswanath, F. Kooti, N. K. Sharma, G. Korlam, F. Benevenuto, and K. P. Gummadi, "Understanding and combating link farming in the twitter social network," In Proceedings of the 21st international conference on World Wide Web, pp. 61--70, ACM, April 2012.Google Scholar
- A. Bessi, E. Ferrara, "Social bots distort the 2016 US Presidential election online discussion," First Monday, 21(11), November 2016, Available: https://firstmonday.org/article/view/7090/5653 (visited on July 12, 2019).Google Scholar
- A. Badawy, E. Ferrara, and K. Lerman, "Analyzing the digital traces of political manipulation: The 2016 russian interference twitter campaign," IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), pp. 258--265, IEEE, August 2018.Google ScholarCross Ref
- M. Forelle, P. Howard, A. Monroy-Hernndez and S. Savage, "Political bots and the manipulation of public opinion in Venezuela," arXiv preprint arXiv:1507.07109, July 2015.Google Scholar
- C. A. Davis, O. Varol, E. Ferrara, A. Flammini, and F. Menczer, "Botornot: A system to evaluate social bots," 25th International Conference Companion on World Wide Web, pp. 273--274, International World Wide Web Conferences Steering Committee, April 2016.Google Scholar
- P. C. Lin, and P. M. Huang, "A study of effective features for detecting long-surviving Twitter spam accounts," 15th International Conference on Advanced Communications Technology (ICACT), pp. 841--846, IEEE, January 2013.Google Scholar
- J. Wang, and I. C. Paschalidis, "Botnet detection based on anomaly and community detection," IEEE Transactions on Control of Network Systems, 4(2), pp. 392--404, June 2017.Google ScholarCross Ref
- A. Duh, M. Slak Rupnik, and D. Koroak, "Collective behavior of social bots is encoded in their temporal twitter activity," Big Data, 6(2), pp. 113--123, June 2018.Google ScholarCross Ref
- S. Cresci, R. Di Pietro, M. Petrocchi, A. Spognardi, and M. Tesconi, "The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race," 26th International Conference on World Wide Web Companion, pp. 963--972, International World Wide Web Conferences Steering Committee, April 2017.Google Scholar
- M. Jiang, P. Cui, A. Beutel, C. Faloutsos, and S. Yang, "Inferring lockstep behavior from connectivity pattern in large graphs," Knowledge and Information Systems, 48(2), pp. 399--428, August 2016.Google ScholarDigital Library
- R. Yu, X. He, and Y. Liu, "Glad: group anomaly detection in social media analysis," ACM Transactions on Knowledge Discovery from Data (TKDD), 10(2), Article no. 18, October 2015.Google ScholarDigital Library
- S. Cresci, R. Di Pietro, M. Petrocchi, A. Spognardi, and M. Tesconi, "DNA-inspired online behavioral modeling and its application to spambot detection," IEEE Intelligent Systems, 31(5), pp. 58--64, September 2016.Google ScholarCross Ref
- F. Morstatter, L. Wu, T. H. Nazer, K. M. Carley, and H. Liu, "A new approach to bot detection: striking the balance between precision and recall," 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), pp. 533--540, IEEE, August 2016.Google Scholar
- P. De Meo, E. Ferrara, G. Fiumara, and A. Provetti, "Generalized louvain method for community detection in large networks," In 2011 11th International Conference on Intelligent Systems Design and Applications, pp. 88--93, IEEE, November 2011.Google Scholar
- S. Iyer, T. Killingback, B. Sundaram, and Z. Wang, "Attack robustness and centrality of complex networks," PloS one, 8(4), e59613, April 2013.Google ScholarCross Ref
- T. Khaund, K. K. Bandeli, M. N. Hussain, A. Obadimu, S. Al-Khateeb, and N. Agarwal, "Analyzing Social and Communication Network Structures of Social Bots and Humans," 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), pp. 794--797, IEEE, August 2018.Google Scholar
- C. Morselli, and J. Roy, "Brokerage qualifications in ringing operations," Criminology, 46(1), pp. 71--98, February 2008.Google ScholarCross Ref
Recommendations
Red Bots Do It Better:Comparative Analysis of Social Bot Partisan Behavior
WWW '19: Companion Proceedings of The 2019 World Wide Web ConferenceRecent research brought awareness of the issue of bots on social media and the significant risks of mass manipulation of public opinion in the context of political discussion. In this work, we leverage Twitter to study the discourse during the 2018 US ...
Discovering social bots on Twitter: a thematic review
The onset of online social networks (OSN) like Twitter became a predominant platform for social expression and public relations. Twitter had 330 million monthly active users by the year 2019. With the gain in popularity, the ratio of virulent and ...
Real-time Detection of Content Polluters in Partially Observable Twitter Networks
WWW '18: Companion Proceedings of the The Web Conference 2018Content polluters, or bots that hijack a conversation for political or advertising purposes are a known problem for event prediction, election forecasting and when distinguishing real news from fake news in social media data. Identifying this type of ...
Comments