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Characterizing the 2016 Russian IRA influence campaign

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Abstract

Until recently, social media were seen to promote democratic discourse on social and political issues. However, this powerful communication ecosystem has come under scrutiny for allowing hostile actors to exploit online discussions in an attempt to manipulate public opinion. A case in point is the ongoing U.S. Congress investigation of Russian interference in the 2016 U.S. election campaign, with Russia accused of, among other things, using trolls (malicious accounts created for the purpose of manipulation) and bots (automated accounts) to spread propaganda and politically biased information. In this study, we explore the effects of this manipulation campaign, taking a closer look at users who re-shared the posts produced on Twitter by the Russian troll accounts publicly disclosed by U.S. Congress investigation. We collected a dataset of 13 million election-related posts shared on Twitter in the year of 2016 by over a million distinct users. This dataset includes accounts associated with the identified Russian trolls as well as users sharing posts in the same time period on a variety of topics around the 2016 elections. We use label propagation to infer the users’ ideology based on the news sources they share. We are able to classify a large number of the users as liberal or conservative with precision and recall above 84%. Conservative users who retweet Russian trolls produced significantly more tweets than liberal ones, about 8 times as many in terms of tweets. Additionally, trolls’ position in the retweet network is stable overtime, unlike users who retweet them who form the core of the election-related retweet network by the end of 2016. Using state-of-the-art bot detection techniques, we estimate that about 5% and 11% of liberal and conservative users are bots, respectively. Text analysis on the content shared by trolls reveal that conservative trolls talk about refugees, terrorism, and Islam, while liberal trolls talk more about school shootings and the police. Although an ideologically broad swath of Twitter users were exposed to Russian trolls in the period leading up to the 2016 U.S. Presidential election, it is mainly conservatives who help amplify their message.

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Notes

  1. https://www.recode.net/2017/11/2/16598312/russia-Twitter-trump-Twitter-deactivated-handle-list.

  2. https://www.crimsonhexagon.com/.

  3. https://www.allsides.com/media-bias/media-bias-ratings.

  4. https://mediabiasfactcheck.com/.

  5. We use the algorithm in the Python version of the Igraph library (Csardi and Nepusz 2006).

References

  • Adamic LA, Glance N (2005) The political blogosphere and the 2004 US election: divided they blog. In: Proceedings of the 3rd international workshop on Link discovery

  • Agarwal A, Xie B, Vovsha I, Rambow O, Passonneau R (2011) Sentiment analysis of Twitter data. In: Proceedings of the workshop on languages in social media. Association for Computational Linguistics, pp 30–38

  • Alarifi A, Alsaleh M, Al-Salman A (2016) Twitter turing test: identifying social machines. Inf Sci 372:332–346

    Article  Google Scholar 

  • Aral S, Walker D (2012) Identifying influential and susceptible members of social networks. Science 337(6092):337–341

    Article  MathSciNet  Google Scholar 

  • Aral S, Muchnik L, Sundararajan A (2009) Distinguishing influence-based contagion from homophily-driven diffusion in dynamic networks. Proc Natl Acad Sci 106(51):21544–21549

    Article  Google Scholar 

  • Badawy A, Ferrara E, Lerman K (2018) Analyzing the digital traces of political manipulation: the 2016 Russian interference Twitter campaign. In: ASONAM

  • Bakshy E, Hofman J, Mason W, Watts D (2011) Everyone’s an influencer: quantifying influence on Twitter. In: 4th WSDM

  • Bakshy E, Messing S, Adamic LA (2015) Exposure to ideologically diverse news and opinion on Facebook. Science 348(6239):1130–1132

    Article  MathSciNet  Google Scholar 

  • Barberá P, Wang N, Bonneau R, Jost JT, Nagler J, Tucker J, González-Bailón S (2015) The critical periphery in the growth of social protests. PLoS ONE 10(11):e0143611

    Article  Google Scholar 

  • Bekafigo MA, McBride A (2013) Who tweets about politics? Political participation of Twitter users during the 2011 gubernatorial elections. Soc Sci Comput Rev 31(5):625–643

    Article  Google Scholar 

  • Bessi A, Ferrara E (2016) Social bots distort the 2016 us presidential election online discussion. First Monday. https://doi.org/10.5210/fm.v21i11.7090

    Article  Google Scholar 

  • Bond RM, Fariss CJ, Jones JJ, Kramer AD, Marlow C, Settle JE, Fowler JH (2012) A 61-million-person experiment in social influence and political mobilization. Nature 489(7415):295

    Article  Google Scholar 

  • Bruns A, Burgess JE (2011) The use of Twitter hashtags in the formation of ad hoc publics. In: 6th ECPR general conference

  • Buckels EE, Trapnell PD, Paulhus DL (2014) Trolls just want to have fun. Personal Individ Differ 67:97–102

    Article  Google Scholar 

  • Carlisle JE, Patton RC (2013) Is social media changing how we understand political engagement? An analysis of Facebook and the 2008 presidential election. Political Res Q 66(4):883–895

    Article  Google Scholar 

  • Centola D (2010) The spread of behavior in an online social network experiment. Science 329(5996):1194–1197

    Article  Google Scholar 

  • Centola D (2011) An experimental study of homophily in the adoption of health behavior. Science 334(6060):1269–1272

    Article  Google Scholar 

  • Conover M, Gonçalves B, Ratkiewicz J, Flammini A, Menczer F (2011a) Predicting the political alignment of Twitter users. In: Proceedings of 3rd IEEE conference on social computing, pp 192–199

  • Conover M, Ratkiewicz J, Francisco MR, Gonçalves B, Menczer F, Flammini A (2011b) Political polarization on Twitter. ICWSM 133:89–96

    Google Scholar 

  • Conover MD, Davis C, Ferrara E, McKelvey K, Menczer F, Flammini A (2013a) The geospatial characteristics of a social movement communication network. PLoS ONE 8(3):e55957

    Article  Google Scholar 

  • Conover MD, Ferrara E, Menczer F, Flammini A (2013b) The digital evolution of occupy wall street. PLoS ONE 8(5):e64679

    Article  Google Scholar 

  • Csardi G, Nepusz T (2006) The igraph software package for complex network research. InterJournal Complex Syst 1695(5):1–9

    Google Scholar 

  • Davis CA, Varol O, Ferrara E, Flammini A, Menczer F (2016) Botornot: a system to evaluate social bots. In: Proceedings of 25th international conference on world wide web, pp 273–274

  • Diakopoulos NA, Shamma DA (2010) Characterizing debate performance via aggregated Twitter sentiment. In: SIGCHI Conference

  • DiGrazia J, McKelvey K, Bollen J, Rojas F (2013) More tweets, more votes: social media as a quantitative indicator of political behavior. PLoS ONE 8(11):e79449

    Article  Google Scholar 

  • Dutt R, Deb A, Ferrara E (2018) ‘Senator, we sell ads’: analysis of the 2016 Russian Facebook ads campaign. In: Third international conference on intelligent information technologies (ICIIT 2018)

  • Effing R, Van Hillegersberg J, Huibers T(2011) Social media and political participation: are Facebook, Twitter and Youtube democratizing our political systems? In: Electronic participation, pp 25–35

  • El-Khalili S (2013) Social media as a government propaganda tool in post-revolutionary Egypt. First Monday. https://doi.org/10.5210/fm.v18i3.4620

    Article  Google Scholar 

  • Enli GS, Skogerbø E (2013) Personalized campaigns in party-centred politics: Twitter and Facebook as arenas for political communication. Inf Commun Soc 16(5):757–774

    Article  Google Scholar 

  • Ferrara E (2017) Disinformation and social bot operations in the run up to the 2017 French presidential election. First Monday. https://doi.org/10.5210/fm.v22i8.8005

    Article  Google Scholar 

  • Ferrara E (2018) Measuring social spam and the effect of bots on information diffusion in social media. In: Lehmann S, Ahn YY (eds) Complex spreading phenomena in social systems. Springer, Cham, pp 229–255

    Chapter  Google Scholar 

  • Ferrara E, Varol O, Davis C, Menczer F, Flammini A (2016a) The rise of social bots. Commun ACM 59(7):96–104

    Article  Google Scholar 

  • Ferrara E, Varol O, Menczer F, Flammini A (2016b) Detection of promoted social media campaigns. In: Tenth international AAAI conference on web and social media, pp 563–566

  • Fourney A, Racz MZ, Ranade G, Mobius M, Horvitz E (2017) Geographic and temporal trends in fake news consumption during the 2016 US presidential election. In: CIKM, vol 17

  • Freitas C, Benevenuto F, Ghosh S, Veloso A (2015) Reverse engineering socialbot infiltration strategies in Twitter. In: 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), pp 25–32

  • Gibson RK, McAllister I (2006) Does cyber-campaigning win votes? Online communication in the 2004 australian election. J Elect Public Opin Parties 16(3):243–263

    Article  Google Scholar 

  • González-Bailón S, Borge-Holthoefer J, Rivero A, Moreno Y (2011) The dynamics of protest recruitment through an online network. Sci Rep 1:197

    Article  Google Scholar 

  • González-Bailón S, Borge-Holthoefer J, Moreno Y (2013) Broadcasters and hidden influentials in online protest diffusion. Am Behav Sci 57(7):943–965

    Article  Google Scholar 

  • Howard P (2006) New media campaigns and the managed citizen. Cambridge University Press, Cambridge

    Google Scholar 

  • Hwang T, Pearce I, Nanis M (2012) Socialbots: voices from the fronts. Interactions 19(2):38–45

    Article  Google Scholar 

  • Kloumann IM, Danforth CM, Harris KD, Bliss CA, Dodds PS (2012) Positivity of the english language. PLoS ONE 7(1):e29484

    Article  Google Scholar 

  • Kollanyi B, Howard PN, Woolley SC (2016) Bots and automation over Twitter during the first us presidential debate. Political Bots

  • Kudugunta S, Ferrara E (2018) Deep neural networks for bot detection. Inf Sci 467(October):312–322

    Article  Google Scholar 

  • Loader BD, Mercea D (2011) Networking democracy? Social media innovations and participatory politics. Inf Commun Soc 14(6):757–769

    Article  Google Scholar 

  • Messias J, Schmidt L, Oliveira R, Benevenuto F (2013) You followed my bot! Transforming robots into influential users in Twitter. First Monday. https://doi.org/10.5210/fm.v18i7.4217

    Article  Google Scholar 

  • Metaxas PT, Mustafaraj E (2012) Social media and the elections. Science 338(6106):472–473

    Article  Google Scholar 

  • Monsted B, Sapiezynski P, Ferrara E, Lehmann S (2017) Evidence of complex contagion of information in social media: an experiment using Twitter bots. PLoS ONE 12(9):1–12

    Article  Google Scholar 

  • Pennycook G, Rand DG (2017) Assessing the effect of “disputed” warnings and source salience on perceptions of fake news accuracy. Social Science Research Network. https://papers.ssrn.com/sol3/papers.cfm

  • Raghavan UN, Albert R, Kumara S (2007) Near linear time algorithm to detect community structures in large-scale networks. Phys Rev E 76(3):036106

    Article  Google Scholar 

  • Ratkiewicz J, Conover M, Meiss M, Gonçalves B, Patil S, Flammini A, Menczer F (2011a) Truthy: mapping the spread of astroturf in microblog streams. In: 20th WWW conference, pp 249–252

  • Ratkiewicz J, Conover M, Meiss MR, Gonçalves B, Flammini A, Menczer F (2011b) Detecting and tracking political abuse in social media. In: ICWSM, vol 11, pp 297–304

  • Savage S, Monroy-Hernandez A, öllerer TH (2016) Botivist: calling volunteers to action using online bots. In: 19th CSCW

  • Shirky C (2011) The political power of social media: technology, the public sphere, and political change. Foreign Aff 90:28–41

    Google Scholar 

  • Shorey S, Howard PN (2016) Automation, algorithms, and politics: a research review. Int J Commun 10:5032–5055

    Google Scholar 

  • Stella M, Ferrara E, De Domenico M (2018) Bots increase exposure to negative and inflammatory content in online social systems. Proc Natl Acad Sci 115(49):12435–12440

    Article  Google Scholar 

  • Subrahmanian V, Azaria A, Durst S, Kagan V, Galstyan A, Lerman K, Zhu L, Ferrara E, Flammini A, Menczer F (2016) The DARPA Twitter bot challenge. Computer 49(6):38–46

    Article  Google Scholar 

  • Tufekci Z (2014) Big questions for social media big data: representativeness, validity and other methodological pitfalls. In: ICWSM

  • Tufekci Z, Wilson C (2012) Social media and the decision to participate in political protest: observations from Tahrir Square. J Commun 62(2):363–379

    Article  Google Scholar 

  • Varol O, Ferrara E, Ogan CL, Menczer F, Flammini A (2014) Evolution of online user behavior during a social upheaval. In: Proceedings of the 2014 ACM conference on web science

  • Varol O, Ferrara E, Davis C, Menczer F, Flammini A (2017a) Online human–bot interactions: detection, estimation, and characterization. In: ICWSM, pp 280–289

  • Varol O, Ferrara E, Menczer F, Flammini A (2017b) Early detection of promoted campaigns on social media. EPJ Data Sci 6(13):13

    Article  Google Scholar 

  • Warriner AB, Kuperman V, Brysbaert M (2013) Norms of valence, arousal, and dominance for 13,915 english lemmas. Behav Res Methods 45(4):1191–1207

    Article  Google Scholar 

  • Wilson T, Wiebe J, Hoffmann P (2005) Recognizing contextual polarity in phrase-level sentiment analysis. In: Proceedings of the conference on human language technology and empirical methods in natural language processing. Association for Computational Linguistics, pp 347–354

  • Woolley SC, Howard PN (2016) Automation, algorithms, and politics: introduction. Int J Commun 10:9

    Google Scholar 

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Acknowledgements

The authors gratefully acknowledge support by the Air Force Office of Scientific Research (Award #FA9550-17-1-0327). The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of AFOSR or the U.S. Government.

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Authors and Affiliations

  1. USC Information Sciences Institute, Los Angeles, USA

    Adam Badawy, Kristina Lerman & Emilio Ferrara

  2. University of Illinois at Urbana-Champaign, Champaign, USA

    Aseel Addawood

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  1. Adam Badawy
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Correspondence to Adam Badawy.

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Badawy, A., Addawood, A., Lerman, K. et al. Characterizing the 2016 Russian IRA influence campaign. Soc. Netw. Anal. Min. 9, 31 (2019). https://doi.org/10.1007/s13278-019-0578-6

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