The most prevalent form of malware on social media networks is thought to be bots (Aldayel and Magdy 2022 Cai, Li, and Zengi 2017b).

As social networks' popularity grows combined with the availability of vast personal information that users share makes the same valuable features of social platforms for ordinary people a tempting target for malicious entities (Adikari and Dutta 2020). Whereas professional communities use LinkedIn. While Instagram usage is mainly by celebrities and businesses for marketing (Meshram et al. For instance, Twitter is known for being the most famous microblogging social network for receiving rapid updates and breaking news. Different social networks offer a unique value chain and target different user segments. It radically impacts daily human social interactions where users and their communities are the base for online growth, commerce, and information sharing. In this modern world, OSNs such as Twitter, Facebook, Instagram, LinkedIn have become a crucial part of each one’s life (Albayati and Altamimi 2019). Furthermore, this study also showcases a brief rundown of the challenges and opportunities encountered in this field, along with prospective research directions and promising angles to explore. Additionally, we provide a thorough breakdown of the extracted feature categories. We bring forth a concise overview of all the supervised, semi-supervised, and unsupervised methods, along with the details of the datasets provided by the researchers. This literature review attempts to compile and compare the most recent advancements in Machine Learning-based techniques for the detection and classification of bots on five primary social media platforms namely Facebook, Instagram, LinkedIn, Twitter, and Weibo. Cybercriminals and researchers are always engaged in an arms race as new and updated bots are created to thwart ever-evolving detection technologies. They are used to exploit vulnerabilities for illicit benefits such as spamming, fake profiles, spreading inappropriate/false content, click farming, hashtag hijacking, and much more. Moreover, such bots pose serious cyber threats and security concerns to society and public opinion. Malicious bots in these platforms are automated or semi-automated entities used in nefarious ways while simulating human behavior.

The availability of the vast amount of information and their open nature attracts the interest of cybercriminals to create malicious bots. In today’s digitalized era, Online Social Networking platforms are growing to be a vital aspect of each individual’s daily life.

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