Coordinated inauthentic behavior and information spreading on twitter
Open accessIntegrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
AI systems could analyze how adversaries' disinformation spreads across modern media and social networks, allowing defense and security policymakers to respond faster and more effectively.
“AI systems could analyze how adversaries’ disinformation spreads across modern media and social networks (Cinelli et al., 2022; Marigliano et al., 2024; Pacheco et al., 2021; Sharma et al., 2021), allowing our defense and security policymakers to respond faster and more effectively.”
From Enhancement of modern defense capabilities by convergence of cognitive neuroscience and artificial intelligence (2026) · cites this paper as Cinelli et al., 2022
Cites
The spread of true and false news online
Vosoughi, S., Roy, D., & Aral, S. (2018) · Science · doi:10.1126/science.aap9559 · Cited by 8,847 · cited 1× · see citing works
“To this end, existing results are controversial, with some studies reporting a significant contribution of social bots in the spread of low-credibility content [14] and the extremization of online communities [38], while other studies only reported a marginal role [39].”
The spreading of misinformation online
Del Vicario, M., Bessi, A., Zollo, F., Petroni, F., Scala, A., Caldarelli, G., Stanley, H. E., & Quattrociocchi, W. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1517441113 · Cited by 2,407 · see citing works
Self-citationFilter Bubbles, Echo Chambers, and Online News Consumption
Flaxman, S., Goel, S., & Rao, J. M. (2016) · Public Opinion Quarterly · doi:10.1093/poq/nfw006 · Cited by 1,992 · see citing works
Disinformation’s spread: bots, trolls and all of us
Starbird, K. (2019) · Nature · doi:10.1038/d41586-019-02235-x · Cited by 203 · see citing works
The False positive problem of automatic bot detection in social science research
Rauchfleisch, A., & Kaiser, J. (2020) · PLOS ONE · doi:10.1371/journal.pone.0241045 · Cited by 184 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Unsupervised detection of coordinated fake-follower campaigns on social media
Zouzou, Y., & Varol, O. (2024) · EPJ Data Science · doi:10.1140/epjds/s13688-024-00499-6 · Cited by 19 · see citing works
Emotionally based strategic communications as a new tool in defensive cognitive warfare
Ćosić, K., Popović, S., & Wiederhold, B. (2026) · Frontiers in Psychology · doi:10.3389/fpsyg.2026.1751406 · Cited by 4 · see citing works
Cosic, K., Kuljanac, A., & Palermo, S. (2026) · Frontiers in psychology · doi:10.3389/fpsyg.2026.1821242 · Cited by 3 · see citing works
“AI systems could analyze how adversaries’ disinformation spreads across modern media and social networks (Cinelli et al., 2022; Marigliano et al., 2024; Pacheco et al., 2021; Sharma et al., 2021), allowing our defense and security policymakers to respond faster and more effectively.”
- Authors
- Cinelli, M., Cresci, S., Quattrociocchi, W., Tesconi, M., & Zola, P.
- Affiliations
- Sapienza University of Rome (IT)
- Institute of Informatics and Telematics (IT)
- Corresponding author
- Cresci, Stefano
- Venue
- Decision Support Systems
- Publisher
- Elsevier BV
- Published
- 2022-09
- Volume
- 160
- Pages
- 113819
- ISSN
- 0167-9236 (ISSN-L)
- OpenAlex
- W4281620562
- Cited by
- 74
- References
- 5
- Citations by year
- 2026: 22 · 2025: 15 · 2024: 22 · 2023: 12 · 2022: 3
- FWCI
- 4.90
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- Decision Support Systems (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- IRIS Research product catalog (Sapienza University of Rome) (submittedVersion)
- arXiv (Cornell University) (cc-by)
- Topics
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Physical Sciences › Computer Science › Information Systems › Spam and Phishing Detection
- Keywords
- information cascades, information dissemination, Twitter, retweets, algorithmic amplification, misinformation, saturation effects, social media influence
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review