# Social Botomics: A Systematic Ensemble ML Approach for Explainable and Multi-Class Bot Detection
## 2 Related Work
Several studies [37; 38; 39] use Random Forest classifiers and rely on well-established user and content-based features for detection, while also examining the robustness of the utilized features. In addition, another approach [40] enhances the current frameworks by adding behavioral features, in an effort to move away from single account detection and instead track groups of spambots. Finally, [PERSON]. [41] proposes a hybrid approach that primarily takes into account the interactions of a bot account with his followers and combines them with predefined features, constructing classifiers with high accuracy levels. As far as social bots are concerned, a notable effort by [10] constructs various novel datasets that contain social bots which act as fake follower inflators for Twitter accounts. Along with this addition, they simultaneously examine the efficiency of different feature sets in conjunction with several different classifiers. The Random Forest classifier is once more proven dominant. BotOrNot [5; 8; 9; 42], a more refined framework that utilizes over 1000 features distributed in several categories (user, content, network, sentiment) is also presented as a state-of-the-art solution in bot detection. In addition, BotOrNot is one of the few solutions that actually perform multi-class classification of bots. The authors of [28] propose a full-fledged framework that utilizes 1150 features and gives emphasis on the interactions between a genuine human account and a social bot account. More recent efforts [43; 44; 45], move away from a simple classifier detection and instead focus on the malicious effect that social bots have on Twitter, by analyzing political and marketing campaigns or pointing out the biggest challenges in their detection, such as their evolvingbehavior, which largely imitates human actions. In addition, the latest methodologies include subsetting the bot accounts in order to select the best dataset setup for improved classifier accuracy [8] and adversarial algorithms [46; 47] that synthetically create social bot accounts to directly interact with other accounts, learn their strategies and improve detection rates. Apart from works that focus solely on bots, [PERSON]. [13] introduces the distinction between humans, bots and cyborgs (human controlled bots) and proposes an entropy-based framework to classify them. Unsupervised approaches are still gaining ground, thus research ventures are notably limited in comparison with supervised methodologies. Researchers in [48] use statistical inference and behavioral features to trace clusters of extroverted users, labelling them as social bot accounts. Behavioral similarities are also the primary subject of [49] which via unsupervised simulations reveal suspicious activities. An online framework, namely DeBot, that does not require labeled data and is focused on correlated activities between accounts was presented in [50]. The rationale of this approach is that two accounts that appear to have highly similar activities are likely to be bots. The metric of temporal synchronicity is the core aspect of Debot in detecting simultaneous changes in the state of discrete elements. In the same spirit Rtbust [51], is another unsupervised framework that exploits temporal patterns (retweets, mentions, likes) to highlight malicious activity. Recent studies have explored group approaches, that cannot be considered either supervised or unsupervised. Their focus is drawn towards botnets, which can be conceptualized as groups of bots that act together to achieve a common purpose. Notable in these methodologies are network approaches that attempt to detect synchronized and suspicious account behavior [52; 53; 54], analyze the connectivity of such accounts and propose appropriate countermeasures for their detection and deactivation. Despite their robustness and effectiveness, bot detection methodologies have recently been criticized for some crucial deficiencies.