A Decade of Social Bot Detection∗ STEFANO CRESCI, Institute of Informatics and Telematics, National Research Council (IIT-CNR) ACM Reference Format: Stefano Cresci. 2020. A Decade of Social Bot Detection. Commun. ACM 1, 1 (July 2020), 16 pages. https://doi.org/10.1145/ nnnnnnn.nnnnnnn On the morning of November 9th 2016, the world woke up to the shocking outcome of the US Presidential elections: Donald Trump was the 45th President of the United States of America. An unexpected event that still has tremendous consequences all over the world. Today, we know that a minority of social bots – automated social media accounts mimicking humans – played a central role in spreading divisive messages and disinformation, possibly contributing to Trump’s victory [16, 19]. In the aftermath of the 2016 US elections, the world started to realize the gravity of widespread deception in social media. Following Trump’s exploit, we witnessed to the emergence of a strident dissonance between the multitude of efforts for detecting and removing bots, and the increasing effects that these malicious actorsseem to have on our societies [27, 29]. This paradox opens a burning question: What strategies should we enforce in order to stop this social bot pandemic? In these times – during the run-up to the 2020 US elections – the question appears as more crucial than ever. Particularly so, also in light of the recent reported tampering of the electoral debate by thousands of AI-powered accounts1. What stroke social, political and economic analysts after 2016 – deception and automation – has been however a matter of study for computer scientists since at least 2010. In this work, we briefly survey the first decade of research in social bot detection. Via a longitudinal analysis, we discuss the main trends of research in the fight against bots, the major results that were achieved, and the factors that make this never-ending battleso challenging. Capitalizing on lessons learned from our extensive analysis, we suggest possible innovations that could give us the upper hand against deception and manipulation. Studying a decade of endeavours at social bot detection can also inform strategies for detecting and mitigating the effects of other – more recent – forms of online deception, such as strategic information operations and political trolls. ∗Forthcoming article accepted for publication in Communications of the ACM. 1https://about.fb.com/news/2019/12/removing-coordinated-inauthentic-behavior-from-georgia-vietnam-and-the-us/ arXiv:2007.03604v2 [cs.CY] 8 Jul 2020 Author’s address: Stefano Cresci, [email protected], Institute of Informatics and Telematics, National Research Council (IIT-CNR), via G. Moruzzi, 1, Pisa, Italy, 56124. 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