Detection of Bots in Social Media: A Systematic Review. Issue 4 (July 2020)

• Record Type: Journal Article
• Authors: Orabi, Mariam; Mouheb, Djedjiga; Al Aghbari, Zaher; Kamel, Ibrahim
• Is Part Of: Information processing & management. Volume 57:Issue 4(2020:Jul.)
• Publication Date: 2020-07
• Subjects: Social Media -- Bot -- Socialbot -- Sybil -- Malicious attack -- Cybersecurity -- Attack detection -- Detection methods taxonomy -- Systematic literature review; Information storage and retrieval systems -- Periodicals; Information science -- Periodicals; Systèmes d'information -- Périodiques; Sciences de l'information -- Périodiques; Information science; Information storage and retrieval systems; Periodicals; 658.4038
• DOI: 10.1016/j.ipm.2020.102250
• Languages: English
• ISSNs: 0306-4573

Abstract

Highlights: A systematic literature review of social media bots detection methods based on Kichenham and Charters guidelines (2007). A refined taxonomy for social media bots detection methods is proposed. Comparisons between current detection methods are represented. Identification of some gaps in this research area to guide future researches. Abstract: 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.