systematic literature review of social media bots detection systems

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Detection of Bots in Social Media: A Systematic Review

Social media bots (automated accounts) attacks are organized crimes that pose potential threats to public opinion, democracy, public health, stock market and other disciplines. While researchers are building many models to detect social media bot accounts, attackers, on the other hand, evolve their bots to evade detection. This everlasting cat and mouse game makes this field vibrant and demands continuous development. To guide and enhance future solutions, this work provides an overview of social media bots attacks, current detection methods and challenges in this area. To the best of our knowledge, this paper is the first systematic review based on a predefined search strategy, which includes literature concerned about social media bots detection methods, published between 2010 and 2019. The results of this review include a refined taxonomy of detection methods, a highlight of the techniques used to detect bots in social media and a comparison between current detection methods. Some of the gaps identified by this work are: the literature mostly focus on Twitter platform only and rarely use methods other than supervised machine learning, most of the public datasets are not accurate or large enough, integrated systems and real-time detection are required, and efforts to spread awareness are needed to arm legitimate users with knowledge.

Method of Detecting Bots on Social Media. A Literature Review

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• First Online: 23 November 2020
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• Botambu Collins 14 ,
• Dinh Tuyen Hoang 14 , 15 ,
• Dai Tho Dang 14 &
• Dosam Hwang 14  

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12496))

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• International Conference on Computational Collective Intelligence

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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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Department of Computer Engineering, Yeungnam University, Gyeongsan, South Korea

Botambu Collins, Dinh Tuyen Hoang, Dai Tho Dang & Dosam Hwang

Faculty of Engineering and Information Technology, Quang Binh University, Đồng Hới, Vietnam

Dinh Tuyen Hoang

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Correspondence to Dosam Hwang .

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Department of Applied Informatics, Wrocław University of Science and Technology, Wroclaw, Poland

Ngoc Thanh Nguyen

Thua Thien Hue Center of Information Technology, Hue, Vietnam

Bao Hung Hoang

Vietnam - Korea University of Information and Communication Technology, University of Da Nang, Da Nang, Vietnam

Cong Phap Huynh

Department of Computer Engineering, Yeungnam University, Gyeungsan, Korea (Republic of)

Dosam Hwang

Bogdan Trawiński

Department of Information Systems, University of Münster, Münster, Germany

Gottfried Vossen

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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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DOI : https://doi.org/10.1007/978-3-030-63007-2_6

Published : 23 November 2020

Publisher Name : Springer, Cham

Print ISBN : 978-3-030-63006-5

Online ISBN : 978-3-030-63007-2

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