# Social Botomics: A Systematic Ensemble ML Approach for Explainable and Multi-Class Bot Detection
## 1 Introduction  
OSNs have gone well beyond information circulation, becoming a vital part of all human activities. The power of OSNs in diverting public opinion can be compared to and is frequently even greater than the power of traditional mass media or other forms of social interaction [1]. Users of varying intentions and origins continuously interact and impact public opinion trends, sometimes with disruptive consequences. The public openness of OSN platforms has given ground to the rise of automated accounts which mimic human behavior. Such accounts, also known as _social bots_, are machine or human controlled software, either benevolent or malevolent, depending on their intentions [2]. The impact of such accounts has attracted the attention of the scientific community, as their spread and reach is constantly increased, while their activities are continuously refined and transformed. Background. Particularly in Twitter, where the target audience is more vocal on differing opinions and ideologies, bots find fertile ground and ample opportunities in planting discord, stealing sensitive data and attracting users for personal gain. In many cases, such as the recent US elections, official government authorities are becoming increasingly aware of the interference of social bots in the voting process, by influencing the opinions of individuals [3]. As OSN users become more engaged and absorbed in using media, different social bots with various intentions, focused on specific purposes, emerge. For example,there may be bot accounts, the primary purpose of which is to continuously spam explicit, questionable or misleading content in an attempt to spread false rumors and news, known as _spam bots_[4; 5; 6; 7]. Another emerging instance of accounts are politically infused bots, namely _political bots_, that get actively involved in elections and political debates in order to sow discord and deconstruct democratic procedures [8; 9; 10; 11; 12]. There are also bot-assisted humans or human-assisted bots, known as _cyborgs_[13] and even _self-declared_ bots[9], which are accounts that announce their bot identity. It is evident that the threat that bots impose on OSNs is alarming as they frequently create practical confrontations between individuals. These threats expand beyond individual harm and can create rafts and divisions between communities, polarizing the public [14; 15]. Social bots have frequently been employed in voicing negative opinions on sensitive matters (e.g., climate change) [16], causing harmful consequences. Moreover, they are also a challenge for the global economy, as their intrusion in internet traffic [17] and online transactions jeopardize the transparency of economic disputes and the protection of global economic data [18; 19]. Characterizing all social bot accounts as ill-willed would be inaccurate, as there are many that are neutral or even beneficial for users [20]. However, the unfortunate reality is that the majority of social bot accounts do not have benign purposes [21]. Challenges. Recent research has shown that in order to avoid being suspended, social bot accounts can potentially learn to adapt in human behaviors; they show evidence of evolution [22] and linger in social media for prolonged periods of time, further expanding their functions. What is also worrying, is the fact that humans seem to have a hard time distinguishing bot from human accounts [23]. Although Twitter itself has put a lot of effort into detecting and removing fake and bot accounts [24; 25; 26], the issue still remains. Thus, detecting and tackling bot accounts is of vital importance for the structure of society to function properly. In accordance with this purpose, existing and recent bibliographies have focused mainly on developing robust frameworks and algorithms that can distinguish human from bot accounts and pinpoint some guidelines for future detection and suspension of such accounts.