Quantifying the Vulnerabilities of the Online Public Square to Adversarial Manipulation Tactics (1907.06130v5)
Abstract: Social media, seen by some as the modern public square, is vulnerable to manipulation. By controlling inauthentic accounts impersonating humans, malicious actors can amplify disinformation within target communities. The consequences of such operations are difficult to evaluate due to the challenges posed by collecting data and carrying out ethical experiments that would influence online communities. Here we use a social media model that simulates information diffusion in an empirical network to quantify the impacts of several adversarial manipulation tactics on the quality of content. We find that the presence of influential accounts, a haLLMark of social media, exacerbates the vulnerabilities of online communities to manipulation. Among the explored tactics that bad actors can employ, infiltrating a community is the most likely to make low-quality content go viral. Such harm can be further compounded by inauthentic agents flooding the network with low-quality, yet appealing content, but is mitigated when bad actors focus on specific targets, such as influential or vulnerable individuals. These insights suggest countermeasures that platforms could employ to increase the resilience of social media users to manipulation.
- Detecting and tracking political abuse in social media. In Proc. 5th International AAAI Conference on Weblogs and Social Media (ICWSM), 2011.
- Social media and the elections. Science, 338(6106):472–473, 2012.
- Examining trolls and polarization with a retweet network. In Proc. ACM WSDM Workshop on Misinformation and Misbehavior Mining on the Web, 2018.
- Acting the part: Examining information operations within# BlackLivesMatter discourse. Proceedings of the ACM on Human-Computer Interaction, 2(CSCW):20, 2018.
- The science of fake news. Science, 359(6380):1094–1096, 2018.
- Anatomy of an online misinformation network. PLoS ONE, 13(4):e0196087, 2018.
- Fake news on Twitter during the 2016 U.S. presidential election. Science, 363(6425):374–378, 2019.
- The spread of low-credibility content by social bots. Nature Communications, 9:4787, 2018.
- The spread of true and false news online. Science, 359(6380):1146–1151, 2018.
- Herbert Lin. The existential threat from cyber-enabled information warfare. Bulletin of the Atomic Scientists, 75(4):187–196, 2019.
- The attention economy. Scientific American, 323(6):54–61, Dec 2020.
- How algorithmic popularity bias hinders or promotes quality. Scientific Reports, 8:15951, 2018.
- Quantifying biases in online information exposure. Journal of the Association for Information Science and Technology, 70(3):218–229, 2019.
- Uncovering coordinated networks on social media: Methods and case studies. In Proc. International AAAI Conference on Web and Social Media (ICWSM), volume 15, pages 455–466, 2021.
- The rise of social bots. Comm. ACM, 59(7):96–104, 2016.
- Arming the public with artificial intelligence to counter social bots. Human Behavior and Emerging Technologies, 1(1):48–61, 2019.
- Social bots distort the 2016 U.S. Presidential election online discussion. First Monday, 21(11), 2016.
- Influence of augmented humans in online interactions during voting events. PLOS ONE, 14(5):1–16, 2019.
- Emilio Ferrara. Disinformation and Social Bot Operations in the Run Up to the 2017 French Presidential Election. First Monday, 22(8), 2017.
- Identifying and analyzing cryptocurrency manipulations in social media. Preprint 1902.03110, arXiv, 2019.
- Bots increase exposure to negative and inflammatory content in online social systems. Proceedings of the National Academy of Sciences, 115(49):12435–12440, 2018.
- The role of bot squads in the political propaganda on twitter. Communications Physics, 3:81, 2020.
- Dissecting a social botnet: Growth, content and influence in twitter. In Proc. 18th ACM Conf. on Computer Supported Cooperative Work & Social Computing (CSCW), pages 839–851, 2015.
- On the influence of social bots in online protests. In Emma Spiro and Yong-Yeol Ahn, editors, Social Informatics: Proc. 8th International Conference (SocInfo), Part II, volume 10047 of Lecture Notes in Computer Science, pages 269–278, 2016.
- Experimental evidence for tipping points in social convention. Science, 360(6393):1116–1119, 2018.
- Evidence of complex contagion of information in social media: An experiment using twitter bots. PloS one, 12(9):e0184148, 2017.
- Tackling misinformation: What researchers could do with social media data. HKS Misinformation Review, 1(8), 2020.
- Social media and fake news in the 2016 election. Journal of Economic Perspectives, 31(2):211–236, 2017.
- Kathleen Hall Jamieson. Cyberwar: How Russian Hackers and Trolls Helped Elect a President. Oxford University Press, 2018.
- Analyzing the Digital Traces of Political Manipulation: The 2016 Russian Interference Twitter Campaign. In Proc. IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), pages 258–265, 2018.
- Less than you think: Prevalence and predictors of fake news dissemination on Facebook. Science Advances, 5(1), 2019.
- R. Kelly Garrett. Social media’s contribution to political misperceptions in U.S. Presidential elections. PLOS ONE, 14(3):1–16, 2019.
- Internet Research Agency Twitter activity predicted 2016 U.S. election polls. First Monday, 24(7), 2019.
- Assessing the Russian Internet Research Agency’s impact on the political attitudes and behaviors of American Twitter users in late 2017. Proceedings of the National Academy of Sciences, 117(1):243–250, 2020.
- Exposure to the Russian Internet Research Agency foreign influence campaign on Twitter in the 2016 US election and its relationship to attitudes and voting behavior. Nature Communications, 14(1):62, 2023.
- Shifting attention to accuracy can reduce misinformation online. Nature, 592(7855):590–595, 2021.
- Lazy, not biased: Susceptibility to partisan fake news is better explained by lack of reasoning than by motivated reasoning. Cognition, 188:39–50, 2019.
- Anatomy of an ai-powered malicious social botnet. Preprint 2307.16336, arXiv, 2023.
- Twitter. Twitter’s recommendation algorithm. blog.twitter.com/engineering/en_us/topics/open-source/2023/twitter-recommendation-algorithm, 2023.
- Manipulating Twitter through Deletions. In Proc. Intl. AAAI Conf. on Web and Social Media (ICWSM), volume 16, pages 1029–1039, 2022.
- Comparing information diffusion mechanisms by matching on cascade size. Proceedings of the National Academy of Sciences, 118(46):e2100786118, 2021.
- Measuring user influence in twitter: The million follower fallacy. In Proc. Fourth International AAAI Conference on Weblogs and Social Media, 2010.
- The manufacture of partisan echo chambers by follow train abuse on twitter. In Proceedings of the International AAAI Conference on Web and Social Media, volume 16, pages 1017–1028, 2022.
- Neutral bots probe political bias on social media. Nature Communications, 12:5580, 2021.
- H. Simon. Designing organizations for an information-rich world. In Martin Greenberger, editor, Computers, Communication, and the Public Interest, pages 37–52. The Johns Hopkins Press, Baltimore, 1971.
- John Milton. Areopagitica. Dartmouth’s Milton Reading room. Accessed online at www.dartmouth.edu/~milton/reading_room/areopagitica/text.shtml, 1644.
- James Surowiecki. The wisdom of crowds. Anchor, 2005.
- Scott E Page. The difference: How the power of diversity creates better groups, firms, schools, and societies. Princeton University Press, 2008.
- Herbert Lin. On the Organization of the U.S. Government for Responding to Adversarial Information Warfare and Influence Operations. I/S: A Journal of Law and Policy for the Information Society, 15:1–43, 2019.
- Studying fake news spreading, polarisation dynamics, and manipulation by bots: A tale of networks and language. Computer Science Review, 47:100531, 2023.
- The production of information in the attention economy. Scientific Reports, 5:9452, 2015.
- Experimental study of inequality and unpredictability in an artificial cultural market. Science, 311(5762):854–856, 2006.
- How social influence can undermine the wisdom of crowd effect. Proceedings of the National Academy of Sciences, 108(22):9020–9025, 2011.
- Network dynamics of social influence in the wisdom of crowds. Proceedings of the National Academy of Sciences, 114(26):E5070–E5076, 2017.
- Raymond S Nickerson. Confirmation bias: A ubiquitous phenomenon in many guises. Review of general psychology, 2(2):175, 1998.
- Thomas T. Hills. The dark side of information proliferation. Perspectives on Psychological Science, 14(3):323–330, 2019.
- Information gerrymandering and undemocratic decisions. Nature, 573(7772):117–121, 2019.
- Friendship paradox biases perceptions in directed networks. Nature Communications, 11(1):707, 2020.
- Robust dynamic classes revealed by measuring the response function of a social system. Proceedings of the National Academy of Sciences, 105(41):15649–15653, 2008.
- Characterizing and modeling the dynamics of online popularity. Phys. Rev. Lett., 105(15):158701, 2010.
- Haluk Bingol. Fame emerges as a result of small memory. Physical Review E, 77(3):036118, 2008.
- Bernardo A Huberman. Social computing and the attention economy. Journal of Statistical Physics, 151(1–2):329–339, 2013.
- Novelty and collective attention. Proceedings of the National Academy of Sciences, 104(45):17599–17601, 2007.
- How limited visibility and divided attention constrain social contagion. In Proc. ASE/IEEE International Conference on Social Computing, 2012.
- VIP: Incorporating Human Cognitive Biases in a Probabilistic Model of Retweeting. In Proc. International Conference on Social Computing, Behavioral Modeling and Prediction, 2015.
- Moshe Adler. Stardom and talent. American Economic Review, 75(1):208–12, 1985.
- The spreading of misinformation online. Proceedings of the National Academy of Sciences, 113(3):554–559, 2016.
- Competition among memes in a world with limited attention. Sci. Rep., 2(335), 2012.
- Competition-induced criticality in a model of meme popularity. Physical Review Letters, 112(4):048701, 2014.
- Effects of network structure, competition and memory time on social spreading phenomena. Phys. Rev. X, 6(2):021019, 2016.
- Analytical study of quality-biased competition dynamics for memes in social media. Europhysics Letters, 122(2):28002, 2018.
- Right and left, partisanship predicts (asymmetric) vulnerability to misinformation. HKS Misinformation Review, 1(7), 2021.
- Simulating social media using large language models to evaluate alternative news feed algorithms. Preprint 2310.05984, arXiv, 2023.
- The simple rules of social contagion. Scientific reports, 4(1):4343, 2014.
- Political polarization on twitter. In Proc. 5th International AAAI Conference on Weblogs and Social Media (ICWSM), 2011.
- Partisan asymmetries in online political activity. EPJ Data Science, 1:6, 2012.
- Virality prediction and community structure in social networks. Sci. Rep., 3(2522), 2013.
- Optimal network modularity for information diffusion. Physical review letters, 113(8):088701, 2014.
- The role of inflexible minorities in the breaking of democratic opinion dynamics. Physica A: Statistical Mechanics and its Applications, 381:366–376, 2007.
- Effect of zealotry in high-dimensional opinion dynamics models. Phys. Rev. E, 91:022811, 2015.
- Social consensus through the influence of committed minorities. Phys. Rev. E, 84:011130, 2011.
- Committed activists and the reshaping of status-quo social consensus. Phys. Rev. E, 92:042805, 2015.
- Learning through the grapevine and the impact of the breadth and depth of social networks. Proceedings of the National Academy of Sciences, 119(34):e2205549119, 2022.
- Mathematical modeling of disinformation and effectiveness of mitigation policies. Scientific Reports, 13(1):18735, 2023.
- Reconsidering Tweets: Intervening during Tweet Creation Decreases Offensive Content. In Proceedings of the International AAAI Conference on Web and Social Media, volume 16, pages 477–487, 2022.
- Fighting covid-19 misinformation on social media: Experimental evidence for a scalable accuracy-nudge intervention. Psychological science, 31(7):770–780, 2020.
- L. K. Fazio. Pausing to consider why a headline is true or false can help reduce the sharing of false news. The Harvard Kennedy School Misinformation Review, 1(2), 2020.
- Marshall W. Van Alstyne. A Response to Fake News as a Response to Citizens United. Comm. ACM, 62(8):26–29, 2019.
- Jeffrey Mervis. An internet research project draws conservative ire. Science, 346(6210):686–687, 2014.
- Epidemics and rumours. Nature, 204:1118, 1964.
- Combining interventions to reduce the spread of viral misinformation. Nature Human Behaviour, 6(10):1372–1380, 2022.
- The growing amplification of social media: Measuring temporal and social contagion dynamics for over 150 languages on Twitter for 2009–2020. EPJ Data Science, 10:15, 2021.
- Exposure to social engagement metrics increases vulnerability to misinformation. HKS Misinformation Review, 1(5), 2020.
- Replication Data for: Right and left, partisanship predicts vulnerability to misinformation. Harvard Dataverse, 2020. doi:10.7910/DVN/6CZHH5.
- Alexei Vázquez. Growing network with local rules: Preferential attachment, clustering hierarchy, and degree correlations. Phys. Rev. E, 67:056104, 2003.
- Prevalence of Low-Credibility Information on Twitter During the COVID-19 Outbreak. In Proc. ICWSM Intl. Workshop on Cyber Social Threats (CySoc), 2020.
- OSoMe: The IUNI Observatory on Social Media. PeerJ Computer Science, 2:e87, 2016.
- Bao Tran Truong (7 papers)
- Xiaodan Lou (4 papers)
- Alessandro Flammini (67 papers)
- Filippo Menczer (102 papers)