Towards the future of bot detection: A comprehensive taxonomical review and challenges on Twitter/X

• Published in: Computer networks (Amsterdam, Netherlands : 1999) Netherlands : 1999), 2024-12, Vol.254, p.110808, Article 110808
• Main Authors: Javed, Danish, Jhanjhi, NZ, Khan, Navid Ali, Ray, Sayan Kumar, Mazroa, Alanoud Al, Ashfaq, Farzeen, Das, Shampa Rani
• Format: Article
• Language: English
• Subjects: Bot detection; Harmful Twitter Bots (HTB); Twitter
• ISSN: 1389-1286
• DOI: 10.1016/j.comnet.2024.110808

Harmful Twitter Bots (HTBs) are widespread and adaptable to a wide range of social network platforms. The use of social network bots on numerous social network platforms is increasing. As the popularity and utility of social networking bots grow, the attacks using social network-based automated accounts are getting more coordinated, resulting in crimes that might endanger democracy, the financial market, and public health. HTB designers develop their bots to elude detection while academics create several algorithms to identify social media bot accounts. This field is active and necessitates ongoing improvement due to the never-ending cat-and-mouse game. X, previously known as Twitter, is among the biggest social network platforms that has been plagued by automated accounts. Even though new research is being conducted to tackle this issue, the number of bots on Twitter keeps on increasing. In this research, we establish a robust theoretical foundation in the continuously evolving domain of Harmful Twitter Bot (HTB) detection by analyzing the existing HTB detection techniques. Our research provides an extensive literature review and introduces an enhanced taxonomy that has the potential to help the scientific community form better generalizations for HTB detection. Furthermore, we discuss this domain's obstacles and open challenges to direct and improve future research. As far as we are aware, this study marks the first comprehensive examination of HTB detection that includes articles published between June 2013 and August 2023. The review's findings include a more thorough classification of detection approaches, a spotlight on ways to spot Twitter bots, and a comparison of recent HTB detection methods. Moreover, we provide a comprehensive list of publicly available datasets for HTB detection. As bots evolve, efforts must be made to raise awareness, equip legitimate users with information, and help future researchers in the field of social network bot detection.