The spread of low-credibility content by social bots

The massive spread of digital misinformation has been identified as a major threat to democracies. Communication, cognitive, social, and computer scientists are studying the complex causes for the viral diffusion of misinformation, while online platforms are beginning to deploy countermeasures. Little systematic, data-based evidence has been published to guide these efforts. Here we analyze 14 million messages spreading 400 thousand articles on Twitter during ten months in 2016 and 2017. We find evidence that social bots played a disproportionate role in spreading articles from low-credibility sources. Bots amplify such content in the early spreading moments, before an article goes viral. They also target users with many followers through replies and mentions. Humans are vulnerable to this manipulation, resharing content posted by bots. Successful low-credibility sources are heavily supported by social bots. These results suggest that curbing social bots may be an effective strategy for mitigating the spread of online misinformation.Online misinformation is a threat to a well-informed electorate and undermines democracy. Here, the authors analyse the spread of articles on Twitter, find that bots play a major role in the spread of low-credibility content and suggest control measures for limiting the spread of misinformation.

[1]  Stephen E. Fienberg,et al.  The Comparison and Evaluation of Forecasters. , 1983 .

[2]  Piet Schenelaars,et al.  Public opinion , 2013, BDJ.

[3]  Albert-László Barabási,et al.  Error and attack tolerance of complex networks , 2000, Nature.

[4]  John Langford,et al.  CAPTCHA: Using Hard AI Problems for Security , 2003, EUROCRYPT.

[5]  Rich Caruana,et al.  Predicting good probabilities with supervised learning , 2005, ICML.

[6]  Matthew J. Salganik,et al.  Experimental Study of Inequality and Unpredictability in an Artificial Cultural Market , 2006, Science.

[7]  Markus Jakobsson,et al.  Social phishing , 2007, CACM.

[8]  Calton Pu,et al.  Social Honeypots: Making Friends With A Spammer Near You , 2008, CEAS.

[9]  Ciro Cattuto,et al.  Social spam detection , 2009, AIRWeb '09.

[10]  Cass R. Sunstein,et al.  Going to Extremes: How Like Minds Unite and Divide , 2009 .

[11]  Sushil Jajodia,et al.  Who is tweeting on Twitter: human, bot, or cyborg? , 2010, ACSAC '10.

[12]  Eni Mustafaraj,et al.  From Obscurity to Prominence in Minutes: Political Speech and Real-Time Search , 2010 .

[13]  Kyumin Lee,et al.  Uncovering social spammers: social honeypots + machine learning , 2010, SIGIR.

[14]  D. Wilson,et al.  Going to Extremes: How Like Minds Unite and Divide , 2010 .

[15]  N. Stroud.  Niche News: The Politics of News Choice , 2011 .

[16]  Barbara Poblete,et al.  Information credibility on twitter , 2011, WWW.

[17]  Jacob Ratkiewicz,et al.  Detecting and Tracking Political Abuse in Social Media , 2011, ICWSM.

[18]  Jacob Ratkiewicz,et al.  Political Polarization on Twitter , 2011, ICWSM.

[19]  Jacob Ratkiewicz,et al.  Truthy: mapping the spread of astroturf in microblog streams , 2010, WWW.

[20]  Konstantin Beznosov,et al.  The socialbot network: when bots socialize for fame and money , 2011, ACSAC '11.

[21]  Filippo Menczer,et al.  Partisan asymmetries in online political activity , 2012, EPJ Data Science.

[22]  A. Vespignani,et al.  Competition among memes in a world with limited attention , 2012, Scientific Reports.

[23]  Eli Pariser,et al.  The Filter Bubble: How the New Personalized Web Is Changing What We Read and How We Think , 2012 .

[24]  Kristina Lerman,et al.  How Visibility and Divided Attention Constrain Social Contagion , 2012, 2012 International Conference on Privacy, Security, Risk and Trust and 2012 International Confernece on Social Computing.

[25]  Dan M. Kahan,et al.  Ideology, motivated reasoning, and cognitive reflection , 2013, Judgment and Decision Making.

[26]  Matthew Levendusky.  Why Do Partisan Media Polarize Viewers , 2013 .

[27]  Cong Yu,et al.  Data In, Fact Out: Automated Monitoring of Facts by FactWatcher , 2014, Proc. VLDB Endow..

[28]  P. Resnick,et al.  RumorLens: A System for Analyzing the Impact of Rumors and Corrections in Social Media , 2014 .

[29]  Yongdong Zhang,et al.  News Credibility Evaluation on Microblog with a Hierarchical Propagation Model , 2014, 2014 IEEE International Conference on Data Mining.

[30]  Ponnurangam Kumaraguru,et al.  TweetCred: Real-Time Credibility Assessment of Content on Twitter , 2014, SocInfo.

[31]  Xiaomo Liu,et al.  Real-time Rumor Debunking on Twitter , 2015, CIKM.

[32]  Panagiotis Takis Metaxas,et al.  Using TwitterTrails.com to Investigate Rumor Propagation , 2015, CSCW Companion.

[33]  Guido Caldarelli,et al.  Science vs Conspiracy: Collective Narratives in the Age of Misinformation , 2014, PloS one.

[34]  Filippo Menczer,et al.  Measuring Online Social Bubbles , 2015, 1502.07162.

[35]  Amos Azaria,et al.  The DARPA Twitter Bot Challenge , 2016, Computer.

[36]  Hyun Ah Song,et al.  FRAUDAR: Bounding Graph Fraud in the Face of Camouflage , 2016, KDD.

[37]  Filippo Menczer,et al.  BotOrNot: A System to Evaluate Social Bots , 2016, WWW.

[38]  Emilio Ferrara,et al.  Social Bots Distort the 2016 US Presidential Election Online Discussion , 2016, First Monday.

[39]  Filippo Menczer,et al.  The rise of social bots , 2014, Commun. ACM.

[40]  Peter J Hotez,et al.  Texas and Its Measles Epidemics , 2016, PLoS medicine.

[41]  Filippo Menczer,et al.  Hoaxy: A Platform for Tracking Online Misinformation , 2016, WWW.

[42]  Jeffrey A. Gottfried,et al.  News use across social media platforms 2016 , 2016 .

[43]  G. Caldarelli,et al.  The spreading of misinformation online , 2016, Proceedings of the National Academy of Sciences.

[44]  P. Bower,et al.  Associations between Extending Access to Primary Care and Emergency Department Visits: A Difference-In-Differences Analysis , 2016, PLoS medicine.

[45]  Huan Liu,et al.  Mining Misinformation in Social Media , 2016 .

[46]  Gianluca Stringhini,et al.  The web centipede: understanding how web communities influence each other through the lens of mainstream and alternative news sources , 2017, Internet Measurement Conference.

[47]  Samuel C. Woolley,et al.  Computational propaganda worldwide: Executive summary , 2017 .

[48]  Alireza Sahami Shirazi,et al.  Limited individual attention and online virality of low-quality information , 2017, Nature Human Behaviour.

[49]  Kate Starbird,et al.  Examining the Alternative Media Ecosystem Through the Production of Alternative Narratives of Mass Shooting Events on Twitter , 2017, ICWSM.

[50]  Filippo Menczer,et al.  Online Human-Bot Interactions: Detection, Estimation, and Characterization , 2017, ICWSM.

[51]  Ullrich K. H. Ecker,et al.  Beyond Misinformation: Understanding and coping with the post-truth era , 2017 .

[52]  M. Gentzkow,et al.  Social Media and Fake News in the 2016 Election , 2017 .

[53]  Emilio Ferrara,et al.  Disinformation and Social Bot Operations in the Run Up to the 2017 French Presidential Election , 2017, First Monday.

[54]  Huan Liu,et al.  Detecting Camouflaged Content Polluters , 2017, ICWSM.

[55]  Huan Liu,et al.  Adaptive Spammer Detection with Sparse Group Modeling , 2017, ICWSM.

[56]  Suhang Wang,et al.  Fake News Detection on Social Media: A Data Mining Perspective , 2017, SKDD.

[57]  Filippo Menczer,et al.  Early detection of promoted campaigns on social media , 2017, EPJ Data Science.

[58]  G. Johar,et al.  Perceived social presence reduces fact-checking , 2017, Proceedings of the National Academy of Sciences.

[59]  W. Nuland,et al.  Information operations and Facebook , 2017 .

[60]  Aaron Smith,et al.  Bots in the Twittersphere , 2018 .

[61]  Chris J Vargo,et al.  The agenda-setting power of fake news: A big data analysis of the online media landscape from 2014 to 2016 , 2018, New Media Soc..

[62]  Arkaitz Zubiaga,et al.  Detection and Resolution of Rumours in Social Media , 2017, ACM Comput. Surv..

[63]  B. Morton.  Fake news. , 2018, Marine pollution bulletin.

[64]  Miriam J. Metzger,et al.  The science of fake news , 2018, Science.

[65]  Filippo Menczer,et al.  How algorithmic popularity bias hinders or promotes quality , 2017, Scientific Reports.

[66]  Computational Propaganda , 2018, Oxford Scholarship Online.

[67]  Sinan Aral,et al.  The spread of true and false news online , 2018, Science.

[68]  B. Nyhan,et al.  Selective exposure to misinformation: Evidence from the consumption of fake news during the 2016 U.S. presidential campaign , 2018 .

[69]  Huan Liu,et al.  Tracing Fake-News Footprints: Characterizing Social Media Messages by How They Propagate , 2018, WSDM.

[70]  Daniel M. Smith.  Election , 2018, Dynasties and Democracy.

[71]  Filippo Menczer,et al.  Anatomy of an online misinformation network , 2018, PloS one.

[72]  Filippo Menczer,et al.  Quantifying Biases in Online Information Exposure , 2018, J. Assoc. Inf. Sci. Technol..

[73]  R. Bénabou.  Ideology , 2008, Communication and Capitalism.

[74]  Ursula Smartt,et al.  Social media and fake news , 2020 .