Bots increase exposure to negative and inflammatory content in online social systems
Open accessAbstract
Significance: Social media can deeply influence reality perception, affecting millions of people’s voting behavior. Hence, maneuvering opinion dynamics by disseminating forged content over online ecosystems is an effective pathway for social hacking. We propose a framework for discovering such a potentially dangerous behavior promoted by automatic users, also called “bots,” in online social networks. We provide evidence that social bots target mainly human influencers but generate semantic content depending on the polarized stance of their targets. During the 2017 Catalan referendum, used as a case study, social bots generated and promoted violent content aimed at Independentists, ultimately exacerbating social conflict online. Our results open challenges for detecting and controlling the influence of such content on society.
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The 12-second timing gap constitutes direct evidence of coordinated manipulation analogous to bot farming on human social media platforms.
“The 12-second timing gap constitutes direct evidence of coordinated manipulation analogous to bot farming operations documented on human social media platforms 13,16,22 .”
From The Moltbook Illusion: Separating Human Influence from Emergent Behavior in AI Agent Societies (2026) · cites this paper as [22]
The Discussion (page unavailable) describes bots targeting human influencers and manipulating information exposure on Twitter, supporting the broad analogy to social-media bot manipulation. The Results (page unavailable) discuss activity over time but do not report a 12-second gap or establish that such a gap is direct evidence of coordination; the paper also does not identify its activity as bot farming. (LLM, conf=0.84)
Experimental — accuracy not yet measuredScholars still lack a widespread consensus on the impact of social bots.
“In fact, scholars still lack a widespread consensus on the impact of social bots, with some studies reporting on their pivotal role for increasing disinformation's spread, polarization, and hateful speech [27, 29], and competing results claiming that bots do not play a significant role in these processes [32].”
From A decade of social bot detection (2020) · cites this paper as [29]
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Cites
The anatomy of a large-scale hypertextual Web search engine
Brin, S., & Page, L. (1998) · Computer Networks and ISDN Systems · doi:10.1016/s0169-7552(98)00110-x · Cited by 16,004 · see citing works
Newman, M. (2010) · doi:10.1093/acprof:oso/9780199206650.001.0001 · Cited by 6,595 · see citing works
Schelling, T. (1971) · Journal of Mathematical Sociology · doi:10.1080/0022250x.1971.9989794 · Cited by 4,966 · see citing works
Experimental evidence of massive-scale emotional contagion through social networks
Kramer, A. D. I., Guillory, J. E., & Hancock, J. T. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1320040111 · Cited by 3,687 · see citing works
Lazer, D., Pentland, A., Adamic, L., Aral, S., Barabási, A.-L., Brewer, D., Christakis, N., Contractor, N., Fowler, J., Gutmann, M., et al. (2009) · Science · doi:10.1126/science.1167742 · Cited by 3,365 · see citing works
The spread of behavior in an online social network experiment
Centola, D. (2010) · Science · doi:10.1126/science.1185231 · Cited by 2,692 · see citing works
A 61-million-person experiment in social influence and political mobilization
Bond, R. M., Fariss, C. J., Jones, J. J., Kramer, A. D. I., Marlow, C., Settle, J. E., & Fowler, J. H. (2012) · Nature · doi:10.1038/nature11421 · Cited by 2,575 · see citing works
Ferrara, E., Varol, O., Davis, C., Menczer, F., & Flammini, A. (2016) · Communications of the ACM · doi:10.1145/2818717 · Cited by 1,973 · see citing works
Self-citationAn Experimental Study of the Small World Problem
Travers, J. R., & Milgram, S. (1969) · Sociometry · doi:10.2307/2786545 · Cited by 1,884 · see citing works
The Large‐Scale Structure of Semantic Networks: Statistical Analyses and a Model of Semantic Growth
Steyvers, M., & Tenenbaum, J. B. (2005) · Cognitive Science · doi:10.1207/s15516709cog2901_3 · Cited by 1,396 · see citing works
Political Polarization on Twitter
Conover, M., Ratkiewicz, J., Francisco, M., Goncalves, B., Flammini, A., & Menczer, F. (2011) · Zenodo · doi:10.5281/zenodo.4589064 · Cited by 1,254 · see citing works
Distinguishing influence-based contagion from homophily-driven diffusion in dynamic networks
Aral, S., Muchnik, L., & Sundararajan, A. (2009) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0908800106 · Cited by 1,207 · see citing works
Embodied Cognition: A field guide
Anderson, M. L. (2003) · Artificial Intelligence · doi:10.1016/s0004-3702(03)00054-7 · Cited by 1,129 · see citing works
Identifying influential and susceptible members of social networks
Aral, S., & Walker, D. (2012) · Science · doi:10.1126/science.1215842 · Cited by 1,047 · see citing works
Social bots distort the 2016 U.S. Presidential election online discussion
Bessi, A., & Ferrara, E. (2016) · First Monday · doi:10.5210/fm.v21i11.7090 · Cited by 993 · see citing works
Self-citationOnline Human-Bot Interactions: Detection, Estimation, and Characterization
Varol, O., Ferrara, E., Davis, C., Menczer, F., & Flammini, A. (2017) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v11i1.14871 · Cited by 928 · cited 3× · see citing works
“However, digital systems are not populated only by humans, but also by software-controlled agents, better known as bots, programmed to pursue specific tasks, from sending automated messages to assuming specific social or antisocial behaviors (19, 20).”
Self-citationReprint of: The anatomy of a large-scale hypertextual web search engine
Brin, S., & Page, L. (2012) · Computer Networks · doi:10.1016/j.comnet.2012.10.007 · Cited by 891 · see citing works
A min-max cut algorithm for graph partitioning and data clustering
Ding, C. H. Q., He, X., Hongyuan Zha, B., Gu, M., & Simon, H. D. (2002) · Proceedings · doi:10.1109/icdm.2001.989507 · Cited by 850 · see citing works
Sawyer, R. K. (2005) · Cambridge University Press eBooks · doi:10.1017/cbo9780511734892 · Cited by 732 · see citing works
An Experimental Study of Homophily in the Adoption of Health Behavior
Centola, D. (2011) · Science · doi:10.1126/science.1207055 · Cited by 681 · see citing works
The dynamics of protest recruitment through an online network
González-Bailón, S., Borge-Holthoefer, J., Rivero, A., & Moreno, Y. (2011) · Scientific Reports · doi:10.1038/srep00197 · Cited by 571 · see citing works
Social emergence societies as complex systems
Sawyer, R. K. (2005) · Cited by 535 · see citing works
Disinformation and social bot operations in the run up to the 2017 French presidential election
Ferrara, E. (2017) · First Monday · doi:10.5210/fm.v22i8.8005 · Cited by 524 · see citing works
Self-citation2015 IEEE International Conference on Data Mining
(2015) · doi:10.1109/icdm36327.2015 · Cited by 521 · see citing works
The fundamental advantages of temporal networks
Li, A., Cornelius, S. P., Liu, Y., Wang, L., & Barabási, A.-Ĺ. (2017) · Science · doi:10.1126/science.aai7488 · Cited by 496 · see citing works
The Dynamics of Protest Recruitment through an Online Network
González‐Bailón, S., Borge‐Holthoefer, J., Rivero, A., & Moreno, Y. (2011) · ScholarlyCommons (University of Pennsylvania) · Cited by 466 · see citing works
Social Media, Network Heterogeneity, and Opinion Polarization
Lee, J. K., Choi, J., Kim, C., & Kim, Y. (2014) · Journal of Communication · doi:10.1111/jcom.12077 · Cited by 460 · see citing works
Tsur, O., & Rappoport, A. (2012) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/2124295.2124320 · Cited by 423 · see citing works
Topic sentiment analysis in twitter
Wang, X., Wei, F., Liu, X., Zhou, M. X., & Zhang, M. (2011) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/2063576.2063726 · Cited by 394 · see citing works
Investigating the structure of semantic networks in low and high creative persons
Kenett, Y. N., Anaki, D., & Faust, M. (2014) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2014.00407 · Cited by 392 · see citing works
Baronchelli, A., Ferrer‐i‐Cancho, R., Pastor‐Satorras, R., Chater, N., & Christiansen, M. H. (2013) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2013.04.010 · Cited by 344 · see citing works
Locally noisy autonomous agents improve global human coordination in network experiments
Shirado, H., & Christakis, N. A. (2017) · Nature · doi:10.1038/nature22332 · Cited by 288 · see citing works
Spontaneous emergence of social influence in online systems
Onnela, J.-P. “., & Reed‐Tsochas, F. (2010) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0914572107 · Cited by 249 · see citing works
Flexibility of thought in high creative individuals represented by percolation analysis
Kenett, Y. N., Levy, O., Kenett, D. Y., Stanley, E., Faust, M., & Havlin, S. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1717362115 · Cited by 225 · see citing works
What Do Retweets Indicate? Results from User Survey and Meta-Review of Research
Metaxas, P., Mustafaraj, E., Wong, K., Zeng, L., O'Keefe, M., & Finn, S. (2021) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v9i1.14661 · Cited by 162 · see citing works
Multiplex lexical networks reveal patterns in early word acquisition in children
Stella, M., Beckage, N., & Brede, M. (2017) · Scientific Reports · doi:10.1038/srep46730 · Cited by 131 · see citing works
Self-citationThe dynamics of information-driven coordination phenomena: A transfer entropy analysis
Borge‐Holthoefer, J., Perra, N., Gonçalves, B., González-Bailón, S., Arenas, À., Moreno, Y., & Vespignani, A. (2016) · Science Advances · doi:10.1126/sciadv.1501158 · Cited by 110 · see citing works
Quality versus quantity of social ties in experimental cooperative networks
Shirado, H., Fu, F., Fowler, J. H., & Christakis, N. A. (2013) · Nature Communications · doi:10.1038/ncomms3814 · Cited by 87 · see citing works
Taulé, M., Martí, M. A., Rangel, F., Rosso, P., Bosco, C., & Patti, V. (2017) · Institutional Research Information System University of Turin (University of Turin) · Cited by 55 · see citing works
Building layered, multilingual sentiment lexicons at synset and lemma levels
Cruz, F. L., Troyano, J. A., Pontes, B., & Ortega, F. J. (2014) · Expert Systems with Applications · doi:10.1016/j.eswa.2014.04.005 · Cited by 45 · see citing works
Syntactic networks, do they contribute valid information on syntactic development in children?
Ninio, A. (2014) · Physics of Life Reviews · doi:10.1016/j.plrev.2014.05.004 · Cited by 14 · see citing works
Flagged first, then by citations
Research works printed but not linked: 1
- [35]Newman M 2010 Networks: An Introduction Oxford Univ Press Oxford
Cited by
Sorted by citations
Assessing the risks of ‘infodemics’ in response to COVID-19 epidemics
Gallotti, R., Valle, F., Castaldo, N., Sacco, P., & De Domenico, M. (2020) · Nature Human Behaviour · doi:10.1038/s41562-020-00994-6 · Cited by 591 · see citing works
Self-citationTangcharoensathien, V., Calleja, N., Nguyen, T., Purnat, T., D’Agostino, M., Garcia-Saiso, S., Landry, M., Rashidian, A., Hamilton, C., AbdAllah, A., et al. (2020) · Journal of Medical Internet Research · doi:10.2196/19659 · Cited by 561 · see citing works
Evaluating the fake news problem at the scale of the information ecosystem
Allen, J., Howland, B., Mobius, M., Rothschild, D., & Watts, D. J. (2020) · Science Advances · doi:10.1126/sciadv.aay3539 · Cited by 528 · see citing works
Arming the public with artificial intelligence to counter social bots
Yang, K., Varol, O., Davis, C. A., Ferrara, E., Flammini, A., & Menczer, F. (2019) · Human Behavior and Emerging Technologies · doi:10.1002/hbe2.115 · Cited by 419 · see citing works
“Similar cases of political manipulation were reported in other countries (Stella et al., 2018).”
BackgroundResultSelf-citationSocial Media, Echo Chambers, and Political Polarization
Barberá, P. (2020) · Social Media and Democracy · doi:10.1017/9781108890960.004 · Cited by 373 · see citing works
A systematic review of worldwide causal and correlational evidence on digital media and democracy
Lorenz-Spreen, P., Oswald, L., Lewandowsky, S., & Hertwig, R. (2022) · Nature Human Behaviour · doi:10.1038/s41562-022-01460-1 · Cited by 348 · see citing works
A decade of social bot detection
Cresci, S. (2020) · Communications of the ACM · doi:10.1145/3409116 · Cited by 323 · see citing works
“In fact, scholars still lack a widespread consensus on the impact of social bots, with some studies reporting on their pivotal role for increasing disinformation’s spread, polarization, and hateful speech [28, 30], and competing results claiming that bots do not play a significant role in these processes [33].”
BackgroundEffects of Credibility Indicators on Social Media News Sharing Intent
Yaqub, W., Kakhidze, O., Brockman, M. L., Memon, N., & Patil, S. (2020) · Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3313831.3376213 · Cited by 171 · see citing works
“Some have called for considering elections as Critical National Infrastructure [53] while others have advocated elevating cybersecurity debates to the highest levels of government in order to devise effective policy measures against large-scale manipulation of the electorate via massive propagation of misinformation […”
BackgroundDetection of Novel Social Bots by Ensembles of Specialized Classifiers
Sayyadiharikandeh, M., Varol, O., Yang, K.-C., Flammini, A., & Menczer, F. (2020) · Proceedings of the 29th ACM International Conference on Information & Knowledge Management · doi:10.1145/3340531.3412698 · Cited by 159 · see citing works
The Moral Psychology of Artificial Intelligence
Bonnefon, J.-F., Rahwan, I., & Shariff, A. (2024) · Annual Review of Psychology · doi:10.1146/annurev-psych-030123-113559 · Cited by 151 · see citing works
Quantifying echo chamber effects in information spreading over political communication networks
Cota, W., Ferreira, S. C., Pastor-Satorras, R., & Starnini, M. (2019) · EPJ Data Science · doi:10.1140/epjds/s13688-019-0213-9 · Cited by 146 · see citing works
What types of COVID-19 conspiracies are populated by Twitter bots?
Ferrara, E. (2020) · First Monday · doi:10.5210/fm.v25i6.10633 · Cited by 223 · see citing works
“en referred to as bots (a.k.a., social bots, sybil accounts, etc.). The presence of bots has been documented in the context of online political discussion [1]–[3], public health [4]–[8], civil unrest [9], stock market manipulation [10], the spread of false news [11]–[13], alongside with other tools such as troll accou…”
BackgroundSelf-citationCharting the Landscape of Online Cryptocurrency Manipulation
Nizzoli, L., Tardelli, S., Avvenuti, M., Cresci, S., Tesconi, M., & Ferrara, E. (2020) · IEEE Access · doi:10.1109/access.2020.3003370 · Cited by 132 · see citing works
Self-citationThe Future of False Information Detection on Social Media: New Perspectives and Trends
Guo, B., Ding, Y., Yao, L., Liang, Y., & Yu, Z. (2021) · ACM Computing Surveys · doi:10.1145/3393880 · Cited by 122 · see citing works
Measuring the news and its impact on democracy
Watts, D. J., Rothschild, D. M., & Mobius, M. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1912443118 · Cited by 121 · see citing works
Who Falls for Online Political Manipulation?
Badawy, A., Lerman, K., & Ferrara, E. (2019) · Companion Proceedings of The 2019 World Wide Web Conference · doi:10.1145/3308560.3316494 · Cited by 105 · see citing works
Self-citationInvestigating Italian disinformation spreading on Twitter in the context of 2019 European elections
Pierri, F., Artoni, A., & Ceri, S. (2020) · PLOS ONE · doi:10.1371/journal.pone.0227821 · Cited by 93 · see citing works
Influence of augmented humans in online interactions during voting events
Stella, M., Cristoforetti, M., & De Domenico, M. (2019) · PLOS ONE · doi:10.1371/journal.pone.0214210 · Cited by 51 · see citing works
Self-citationBruno, M., Lambiotte, R., & Saracco, F. (2022) · EPJ Data Science · doi:10.1140/epjds/s13688-022-00330-0 · Cited by 40 · see citing works
Trust within human-machine collectives depends on the perceived consensus about cooperative norms
Makovi, K., Sargsyan, A., Li, W., Bonnefon, J.-F., & Rahwan, T. (2023) · Nature Communications · doi:10.1038/s41467-023-38592-5 · Cited by 36 · see citing works
On manipulation by emotional AI: UK adults’ views and governance implications
Bakir, V., Laffer, A., McStay, A., Miranda, D., & Urquhart, L. (2024) · Frontiers in Sociology · doi:10.3389/fsoc.2024.1339834 · Cited by 34 · see citing works
Investigating Fake and Reliable News Sources Using Complex Networks Analysis
Mazzeo, V., & Rapisarda, A. (2022) · Frontiers in Physics · doi:10.3389/fphy.2022.886544 · Cited by 6 · see citing works
The Moltbook Illusion: Separating Human Influence from Emergent Behavior in AI Agent Societies
Li, N. (2026) · arXiv · doi:10.48550/arxiv.2602.07432 · Cited by 1 · see citing works
“%) occurred on a single day: The 12-second timing gap constitutes direct evidence of coordinated manipulation analogous to bot farming operations documented on human social media platforms 13,16,22 .”
Bail, C. A., Guay, B., Maloney, E., Combs, A., Hillygus, D. S., Merhout, F., Freelon, D., & Volfovsky, A. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1906420116 · Cited by 224 · see citing works
- Authors
- Stella, M., Ferrara, E., & De Domenico, M.
- Affiliations
- Fondazione Bruno Kessler (IT)
- University of Southern California (US)
- Center for Information and Communication Technology, Fondazione Bruno Kessler, 38123 Trento, Italy;
- Corresponding author
- Ferrara, Emilio
- Venue
- Proceedings of the National Academy of Sciences
- Publisher
- National Academy of Sciences
- Published
- 2018-11-20
- Volume
- 115
- Issue
- 49
- Pages
- 12435-12440
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- PMC
- PMC6298098
- OpenAlex
- W2900936184
- Cited by
- 492
- References
- 41
- Citations by year
- 2026: 40 · 2025: 50 · 2024: 44 · 2023: 53 · 2022: 73 · 2021: 70 · 2020: 121 · 2019: 38 · 2018: 1 · 2017: 2
- FWCI
- 130.65
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Proceedings of the National Academy of Sciences (publishedVersion, cc-by-nc-nd)
- Europe PMC (PubMed Central) (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- SSRN Electronic Journal (submittedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Iris (University of Trento) (submittedVersion, cc-by-nc-nd)
- Padua Research Archive (University of Padova) (submittedVersion)
- arXiv (Cornell University)
- Funders
- DOD | USAF | AFMC | Air Force Office of Scientific Research (FA9550-17-1-0327)
- Topics
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Social Sciences › Communication › Social Media and Politics
- Physical Sciences › Computer Science › Information Systems › Spam and Phishing Detection
- Keywords
- social bots, online polarization, Catalan independence referendum, Twitter, political conflict, bot detection
- MeSH terms
- Aggression, Humans, Politics, Spain, Expressed Emotion, Internet, Social Networking, Social Media
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