# Social Botomics: A Systematic Ensemble ML Approach for Explainable and Multi-Class Bot Detection
## 2 Related Work  
Before delving into the developed methods and algorithms for bot detection, it is important to highlight the nature of a bot and its functionalities. According to Botwiki ([[https://botwiki.org/bots/](https://botwiki.org/bots/)]([https://botwiki.org/bots/](https://botwiki.org/bots/)), accessed on 1 October 2021), a bot is \"a software application that runs automated tasks over the Internet. Typically, bots perform tasks that are both simple and structurally repetitive, at a much higher and intense rate than would be possible for a human alone.\" A different interpretation is that a bot is \"a software application that is programmed to do certain tasks\" ([[https://www.cloudflare.com/learning/bots/what-is-a-bot/](https://www.cloudflare.com/learning/bots/what-is-a-bot/)]([https://www.cloudflare.com/learning/bots/what-is-a-bot/](https://www.cloudflare.com/learning/bots/what-is-a-bot/)), accessed on 1 October 2021). Essentially, bots are automated tools that function without the need for human intervention or control, except when control is defined by research objectives [13]. Their purpose is to perform repetitive tasks that would seem mundane or time consuming for humans, exhibiting much faster rates than a human account. A practical and more humanly intuitive notion is that bots are \"predictable automatons that do not have the capacity for emotions, meaning-making, creativity, and sociality\" [27]. Bots exponentially gain ground in OSNs and their dominance is already evident. For example, according to a recent survey, two thirds of the URLs shared in Twitter feeds are posted by bots [28]. As the popularity of bots grows and social media platforms routinely benefit or are harmed by their use, researchers have turned their attention to tracking bots, especially on Twitter. During the past years, there have been various attempts at detecting a bot, or specific categories of bots. In general, the timeline of bot detection spans a decade, with research studies increasing in later years [22]. As the popularity of bots grows and social media platforms routinely benefit or are harmed by their use, researchers have turned their attention to tracking bots, especially on Twitter. This can be further highlighted by the wide influence of bots in presidential elections [29; 30], financial markets [19], pandemic related news [31] and cryptocurrency manipulation [32] as well as the various and different bot categories [2], and particularly social bots [33]. Recent studies have highlighted that the majority of existing literature relies either on ML approaches or unsupervised methodologies in order to construct robust implementations that fulfill this purpose. There have also been attempts that provide web-based tools which gather data related to user accounts on social media from specialized APIs and infer if the account is a bot. In the following we will review the latest and most important bot detection approaches. Regarding supervised approaches and spam bots, Ref. [34] is the first attempted research work that refers to spam bot detection via an ML Classifier. Apart from classifying Twitter accounts as spam bot or human, this work takes the first step towards exploring the major characteristics of spam bot accounts, such as tweet frequency, longevity and networks of followers. Spammers and social bots are also the central point of another paper [35], where the authors construct fake accounts to attract spammer activity and exploit the accumulated data to construct robust classifiers based on textual and network features. However, it is fairly evident that, as detection methodologies evolve, so do bot accounts, which adapt and develop evasion strategies. Thus, [PERSON]. [36] proposes new features that are not affected by evasion strategies and are either graph related (clustering coefficient, betweenness centrality) or neighbor related (followers of neighbors, tweets of neighbors, etc.). This meticulous construction of features eventually yields good results, with low false positive rates.