Abstract
The introduction of the online social media system has unquestionably facilitated communication as well as being a prime and cheap source of information. However, despite these numerous advantages, the social media system remains a double-edged sword. Recently, the online social media ecosystem although fast becoming the primary source of information has become the medium for misinformation and other malicious attacks. These malicious attacks are further exacerbated by the use of social bots that have implacable consequences to victims. In this study, we examine the various methods employed by experts and academia to detect and curb Sybils attack. We define and explain three types of social bots such as the good, the bad and the ugly. We surmised that although the various social media giants have peddled in orthogonal techniques to uncloak and perturb Sybils activities, the adversaries are also working on a robust method to evade detection, hence, a heuristic approach including hybrid crowdsourced-machine learning technique is required to avert future attacks.
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Acknowledgment
This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning (2017R1A2B4009410), and the National Research Foundation of Korea (NRF) grant funded by the BK21PLUS Program (22A20130012009).
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Collins, B., Hoang, D.T., Dang, D.T., Hwang, D. (2020). Method of Detecting Bots on Social Media. A Literature Review. In: Nguyen, N.T., Hoang, B.H., Huynh, C.P., Hwang, D., Trawiński, B., Vossen, G. (eds) Computational Collective Intelligence. ICCCI 2020. Lecture Notes in Computer Science(), vol 12496. Springer, Cham. https://doi.org/10.1007/978-3-030-63007-2_6
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