With more than 1 billion users globally, Facebook is one of the biggest social networks in the current times (Santia et al. It was developed in 2004 initially for students by Mark Zuckerberg. Moving on, Facebook is an online social networking site that makes it convenient for people to connect and share with family and friends. Dated May 2019, there were more than a billion users registered on Instagram, according to collected data (Thejas et al. On the other hand, Instagram is an OSN for sharing photos and videos and is accessible on both Android and iOS since 2012. Over the past few years, Twitter has become a replacement for mainstream media for obtaining news (Wald et al. It is one of the fastest means of circulating information as a result extremely affects people’s perspectives. Twitter allows individuals to express their sentiments on different topics such as entertainment, the stock market, politics, and sports. One of the most widespread and extensively used OSN by people from all walks of life is Twitter. For instance, only 2.375 billion people were using Facebook in the first quarter of 2019 (Siddiqui 2019), thereby representing one-third of the world population (Caers et al. These platforms enable users to produce and exchange user-generated content (Kaplan and Haenlein 2010). On these platforms, user growth and popularity have been increasing at an exponential rate. The social media platforms which are included in the scope of our study are namely Twitter, Facebook, Instagram, LinkedIn, and Weibo. 1, ordered by the number of monthly active users in millions. The most popular social networks globally as of January 2022 are shown in Fig. OSNs have revolutionized communication technologies and are now an essential component of the modern web. 2012) and prevention (Thakur and Breslin 2021). As a result, in recent years, researchers have dedicated a significant amount of attention to social media bot detection (Ali and Syed 2022 Ferrara 2018 Rangel and Rosso 2019 Yang et al. However, the majority of bots are utilized to perform malicious activities such as fabricating accounts, faking engagements, social spamming, phishing, and spreading rumors to manipulate public opinion, such activities not only disturb the genuine users’ experience but also lead to a negative effect on the public’s and individual’s security. 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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