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Who Let The Trolls Out?: Towards Understanding State-Sponsored Trolls

Published: 26 June 2019 Publication History

Abstract

Recent evidence has emerged linking coordinated campaigns by state-sponsored actors to manipulate public opinion on the Web. Campaigns revolving around major political events are enacted via mission-focused ?trolls." While trolls are involved in spreading disinformation on social media, there is little understanding of how they operate, what type of content they disseminate, how their strategies evolve over time, and how they influence the Web's in- formation ecosystem. In this paper, we begin to address this gap by analyzing 10M posts by 5.5K Twitter and Reddit users identified as Russian and Iranian state-sponsored trolls. We compare the behavior of each group of state-sponsored trolls with a focus on how their strategies change over time, the different campaigns they embark on, and differences between the trolls operated by Russia and Iran. Among other things, we find: 1) that Russian trolls were pro-Trump while Iranian trolls were anti-Trump; 2) evidence that campaigns undertaken by such actors are influenced by real-world events; and 3) that the behavior of such actors is not consistent over time, hence detection is not straightforward. Using Hawkes Processes, we quantify the influence these accounts have on pushing URLs on four platforms: Twitter, Reddit, 4chan's Politically Incorrect board (/pol/), and Gab. In general, Russian trolls were more influential and efficient in pushing URLs to all the other platforms with the exception of /pol/ where Iranians were more influential. Finally, we release our source code to ensure the reproducibility of our results and to encourage other researchers to work on understanding other emerging kinds of state-sponsored troll accounts on Twitter.

References

[1]
A. Badawy, K. Lerman, and E. Ferrara. Who Falls for Online Political Manipulation? ArXiv 1808.03281, 2018.
[2]
BBC. Ukraine crisis: Timeline. https://www.bbc.com/news/world-middle-east-26248275, 2014.
[3]
A. Bessi and E. Ferrara. Social bots distort the 2016 US Presidential election online discussion. First Monday, 2016.
[4]
D. M. Blei, A. Y. Ng, and M. I. Jordan. Latent dirichlet allocation. JMLR, 2003.
[5]
V. D. Blondel, J.-L. Guillaume, R. Lambiotte, and E. Lefebvre. Fast unfolding of communities in large networks. Journal of statistical mechanics: theory and experiment, 2008.
[6]
Bloomberg. IRS Cops Are Scouring Crypto Accounts to Build Tax Evasion Cases. https://www.bloomberg.com/news/articles/2018-02-08/irs-cops-scouring-crypto-accounts-to-build-tax-evasion-cases, 2018.
[7]
M. Conover, J. Ratkiewicz, M. R. Francisco, B. Gonçalves, F. Menczer, and A. Flammini. Political Polarization on Twitter. In ICWSM, 2011.
[8]
C. A. Davis, O. Varol, E. Ferrara, A. Flammini, and F. Menczer. BotOrNot: A System to Evaluate Social Bots. In WWW, 2016.
[9]
Department of Justice. Grand Jury Indicts 12 Russian Intelligence Officers for Hacking Offenses Related to the 2016 Election. https://goo.gl/SCyrm6, 2018.
[10]
Department of Justice. Russian National Charged with Interfering in U.S. Political System. https://goo.gl/HAUehB, 2018.
[11]
R. Dutt, A. Deb, and E. Ferrara. 'Senator, We Sell Ads': Analysis of the 2016 Russian Facebook Ads Campaign. ArXiv 1809.10158, 2018.
[12]
S. Earle. TROLLS, BOTS AND FAKE NEWS. https://goo.gl/nz7E8r, 2017.
[13]
A. Elyashar, J. Bendahan, and R. Puzis. Is the Online Discussion Manipulated? Quantifying the Online Discussion Authenticity within Online Social Media. CoRR, abs/1708.02763, 2017.
[14]
E. Ferrara. Disinformation and social bot operations in the run up to the 2017 French presidential election. ArXiv 1707.00086, 2017.
[15]
E. Ferrara, O. Varol, C. A. Davis, F. Menczer, and A. Flammini. The rise of social bots. Commun. ACM, 2016.
[16]
J. Finkelstein, S. Zannettou, B. Bradlyn, and J. Blackburn. A Quantitative Approach to Understanding Online Antisemitism. ArXiv 1809.01644, 2018.
[17]
C. Flores-Saviaga, B. C. Keegan, and S. Savage. Mobilizing the trump train: Understanding collective action in a political trolling community. In ICWSM '18, 2018.
[18]
V. Gadde and Y. Roth. Enabling further research of information operations on Twitter. https://blog.twitter.com/official/en_us/topics/company/2018/enabling-further-research-of-information-operations-on-twitter.html, 2018.
[19]
S. Hegelich and D. Janetzko. Are Social Bots on Twitter Political Actors? Empirical Evidence from a Ukrainian Social Botnet. In ICWSM, 2016.
[20]
G. E. Hine, J. Onaolapo, E. De Cristofaro, N. Kourtellis, I. Leontiadis, R. Samaras, G. Stringhini, and J. Blackburn. Kek, Cucks, and God Emperor Trump: A Measurement Study of 4chan's Politically Incorrect Forum and Its Effects on the Web. In ICWSM '17, 2017.
[21]
P. N. Howard and B. Kollanyi. Bots, #StrongerIn, and #Brexit: Computational Propaganda during the UK-EU Referendum. CoRR, abs/1606.06356, 2016.
[22]
B. Hubbard. Iranian Protesters Ransack Saudi Embassy After Execution of Shiite Cleric. https://nyti.ms/1P7RKUZ, 2016.
[23]
Independent. St Petersburg 'troll farm' had 90 dedicated staff working to influence US election campaign. https://ind.pn/2yuCQdy, 2017.
[24]
IUVM. IUVM's About page. https://iuvm.org/en/about/, 2015.
[25]
M. Jacomy, T. Venturini, S. Heymann, and M. Bastian. ForceAtlas2, a continuous graph layout algorithm for handy network visualization designed for the Gephi software. PloS one, 2014.
[26]
Julian Borger and Saeed Dehghan. Geneva talks end without deal on Iran's nuclear programme. https://www.theguardian.com/world/2013/nov/10/iran-nuclear-deal-stalls-reactor-plutonium-france, 2013.
[27]
S. Kumar, J. Cheng, J. Leskovec, and V. S. Subrahmanian. An Army of Me: Sockpuppets in Online Discussion Communities. In WWW, 2017.
[28]
H. T. Le, G. R. Boynton, Y. Mejova, Z. Shafiq, and P. Srinivasan. Revisiting The American Voter on Twitter. In CHI, 2017.
[29]
S. W. Linderman and R. P. Adams. Discovering Latent Network Structure in Point Process Data. In ICML, 2014.
[30]
S. W. Linderman and R. P. Adams. Scalable Bayesian Inference for Excitatory Point Process Networks. ArXiv 1507.03228, 2015.
[31]
T. Mihaylov, G. Georgiev, and P. Nakov. Finding Opinion Manipulation Trolls in News Community Forums. In CoNLL, 2015.
[32]
T. Mihaylov and P. Nakov. Hunting for Troll Comments in News Community Forums. In ACL, 2016.
[33]
Pushshift. Reddit Dumps. https://files.pushshift.io/reddit/, 2018.
[34]
A. Rahimi, T. Cohn, and T. Baldwin. pigeo: A python geotagging tool. ACL, 2016.
[35]
J. Ratkiewicz, M. Conover, M. R. Meiss, B. Gonçalves, A. Flammini, and F. Menczer. Detecting and Tracking Political Abuse in Social Media. In ICWSM, 2011.
[36]
Reddit. Reddit's 2017 transparency report and suspect account findings. https://www.reddit.com/r/announcements/comments/8bb85p/reddits_2017_transparency_report_and_suspect/, 2018.
[37]
C. M. Rivers and B. L. Lewis. Ethical research standards in a world of big data. F1000Research, 3, 2014.
[38]
Y. Roth. Empowering further research of potential information operations. https://blog.twitter.com/en_us/topics/company/2019/further_research_information_operations.html, 2019.
[39]
S. Zannettou et al. Interactive Graph of Hashtags - Iranian trolls on Twitter. https://trollspaper2018.github.io/trollspaper.github.io/index.html#iranians_graph.gexf, 2018.
[40]
S. Zannettou et al. Interactive Graph of Hashtags - Russian trolls on Twitter. https://trollspaper2018.github.io/trollspaper.github.io/index.html#russians_graph.gexf, 2018.
[41]
S. Zannettou et al. Source code. https://github.com/zsavvas/trolls_analysis, 2019.
[42]
K. Starbird. Examining the Alternative Media Ecosystem Through the Production of Alternative Narratives of Mass Shooting Events on Twitter. In ICWSM, 2017.
[43]
L. Steward, A. Arif, and K. Starbird. Examining Trolls and Polarization with a Retweet Network. In MIS2, 2018.
[44]
G. Stringhini, G. Wang, M. Egele, C. Kruegel, G. Vigna, H. Zheng, and B. Y. Zhao. Follow the green: growth and dynamics in twitter follower markets. In IMC, 2013.
[45]
O. Varol, E. Ferrara, C. A. Davis, F. Menczer, and A. Flammini. Online Human-Bot Interactions: Detection, Estimation, and Characterization. In ICWSM, 2017.
[46]
O. Varol, E. Ferrara, F. Menczer, and A. Flammini. Early detection of promoted campaigns on social media. EPJ Data Science, 2017.
[47]
S. Volkova and E. Bell. Account Deletion Prediction on RuNet: A Case Study of Suspicious Twitter Accounts Active During the Russian-Ukrainian Crisis. In NAACL-HLT, 2016.
[48]
Wikipedia. Republican National Convention. https://en.wikipedia.org/wiki/2016_Republican_National_Convention, 2016.
[49]
Wikipedia. Unite the Right rally. https://en.wikipedia.org/wiki/Unite_the_Right_rally, 2017.
[50]
F. M. F. Wong, C.-W. Tan, S. Sen, and M. Chiang. Quantifying Political Leaning from Tweets and Retweets. In ICWSM, 2013.
[51]
X. Yang, B.-C. Chen, M. Maity, and E. Ferrara. Social Politics: Agenda Setting and Political Communication on Social Media. In SocInfo, 2016.
[52]
S. Zannettou, B. Bradlyn, E. De Cristofaro, M. Sirivianos, G. Stringhini, H. Kwak, and J. Blackburn. What is Gab? A Bastion of Free Speech or an Alt-Right Echo Chamber? In WWW Companion, 2018.
[53]
S. Zannettou, T. Caulfield, J. Blackburn, E. De Cristofaro, M. Sirivianos, G. Stringhini, and G. Suarez-Tangil. On the Origins of Memes by Means of Fringe Web Communities. In IMC, 2018.
[54]
S. Zannettou, T. Caulfield, E. De Cristofaro, N. Kourtellis, I. Leontiadis, M. Sirivianos, G. Stringhini, and J. Blackburn. The Web Centipede: Understanding How Web Communities Influence Each Other Through the Lens of Mainstream and Alternative News Sources. In IMC, 2017.
[55]
S. Zannettou, M. Sirivianos, J. Blackburn, and N. Kourtellis. The web of false information: Rumors, fake news, hoaxes, clickbait, and various other shenanigans. arXiv preprint arXiv:1804.03461, 2018.

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cover image ACM Conferences
WebSci '19: Proceedings of the 10th ACM Conference on Web Science
June 2019
395 pages
ISBN:9781450362023
DOI:10.1145/3292522
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 on the first 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]

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Published: 26 June 2019

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Author Tags

  1. 4chan
  2. disinformation
  3. gab
  4. reddit
  5. social networks
  6. trolls
  7. twitter

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WebSci '19: 11th ACM Conference on Web Science
June 30 - July 3, 2019
Massachusetts, Boston, USA

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WebSci '19 Paper Acceptance Rate 41 of 130 submissions, 32%;
Overall Acceptance Rate 245 of 933 submissions, 26%

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  • (2024)Coordinated Behavior in Information Operations on TwitterIEEE Access10.1109/ACCESS.2024.339348212(61568-61585)Online publication date: 2024
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