Title: Discovering social bots on Twitter: a thematic review
Authors: Rosario Gilmary; Akila Venkatesan; Govindasamy Vaiyapuri
Addresses: Department of Computer Science and Engineering, Pondicherry Engineering College, Pondicherry, India ' Department of Computer Science and Engineering, Pondicherry Engineering College, Pondicherry, India ' Department of Information Technology, Pondicherry Engineering College, Pondicherry, India
Abstract: 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 automated accounts has also increased. It is estimated that 48 million of its functioning accounts are bots. Precisely, Twitter bots or Sybil accounts are kinds of automated web robot software that regulate activities like the tweet, retweet, like or follow via Twitter API. These bots misguide and delude genuine users by spreading spurious content. Hence, uncovering malicious bots from authentic users is obligatory to ensure a safe environment on Twitter. In this paper, the nature of Twitter bots and features of bot detection are discussed with their efficiency. Various bot detection approaches are classified based on the attributes and techniques used. Strong and weak aspects of distinct features and techniques are discussed. Key challenges and future research directions in detecting social bots are also presented. Special reference has been emphasised to contemporary emerging trends.
Keywords: social media; Twitter; OSN security; online social networks security; social bots; categorisation; suspicious behaviour detections; bot detection techniques.
International Journal of Internet Technology and Secured Transactions, 2021 Vol.11 No.4, pp.369 - 395
Received: 08 Apr 2020
Accepted: 23 Jun 2020
Published online: 01 Aug 2021 *