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A Decade of Social Bot Detection∗

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A Decade of Social Bot Detection∗ 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. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation onthefirst page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]. © 2020 Association for Computing Machinery. 1557-7317/2020/7-ART $15.00 https://doi.org/10.1145/nnnnnnn.nnnnnnn Commun. ACM, Vol. 1, No. 1, Article . Publication date: July 2020. 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 M R D A C F B H G K M N S S F S B L N o e u c t c a i b e a e e e o e o u c c u r r c h h t r s g l u y o l r K s k l n e k s k ó w e e ä u t a e d k t e e l a q o i o n a r r f z l c e l h e a l r e i e l i u v s f - i v c h w r e s o a G e e r e i " t " e e h a e t o s d C r i D t t r e u e c y t a r t t a n a e a a o t i d e z t z j a g a l e i e t l l m . m e a t r a e l . i l t e e t t . a . " t l a l a e t " " p t . H á a t . a " " l D R a l l t u a . P " T n l a . " a l o . l p J . e e J t a l " " l . h o l a w . A a " . r e . " u C t d l " e l P e o t C . " i p " t " n r U i t h F t o D c b p B e " o i a o B o k a c D s i r t n o a l n o e n i m o a l i s r i n n m t a t g . i n e e t t ’ s i m o l s s s e g c e p l a " g x s s b a c D a c w p u d n 2 i e i e b i o n o t n s t l a r t o e d 0 s i c o t i a b b e o n c b l p s t a 1 n t t t i e s . n o p a g i o b u s t i . , 4 n . a o c c . t . a n . t o l b " i . g . a s a o u . t m . G " . t . e i " . n S s t n n r . " a a . e P " " t i e e " " d I g t t o n o n A R y e n r O O n s O s O c o d n t f s . s t e . u o r , t . I I . c I I o o a S i . I I t r . s I t T c I C a " o . " a h c c C C s c i C w C n p . a C o P l i . k P i i . a . O O r a O . i l O m . i . r J . o C . r " . l " . o . o . M M . m M . M . m " o P o " . P c " " . " c " . P m P f B . p P r . P P " r P P P o B & o C W R l R i A R P R r p e r r g r c c i o o o o o g C O O x S r O . O W . R c D c c o c m A M o P P I D . N P . P . c e E a W c m S W I I . I a e v t R R E C C C C R R a O M 2 t t e C 3 a w E 3 e e W W W S e e 2 5 N b I 7 0 p p o o p C p ( 5 C ( I S S S S ( A 1 ( o o m S 4 r o o ( 1 1 A W k M M 4 4 c r M r M ) r r I ) ) p t t s . I t . i t ) ) . A S J F N M U C R C S W R F o u e r d y t a o o r n i a n r a e á e l l h s s r r a e e t e u l r s s a h t e l s e u l r s a i g c t a r n n l i z a d e l a h i " e o e g - a " t o t H e S t t S t " y e e a e e t D a e " a a a o t r l t t C a c r i l . k l c a s . r . a e a l k o i " e . i a l a " " n i l t l P . m n B . " D t l e f " a T o o g o " " m o T p e t l D C p h , r . t u " h t a m e s u e e b O e h " t e l d l A a c o t a . a i a p n e n . t t i t r " t . r n i i a s c n c i a . n I o t o " n fl . r h t r e c g n . a S i a n e u c o . e w t a " n d e a o i e p n I a . l d i i n . r s c n o n a g . n i p o e t " z c l fl p i m M d i r B n f i J t e u o p n e i f b - u c e s e p o r g o s x a o a l o o d n .
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