Cited by 32Misinformation and Its ImpactsEuropean and Russian Geopolitical Military StrategiesSocial Media and Politics

The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.

Analyzing digital propaganda and conflict rhetoric: a study on Russia’s bot-driven campaigns and counter-narratives during the Ukraine crisis

Open access
Marigliano, Rebecca, Ng, Lynnette Hui Xian, Carley, Kathleen M.·2024·Social Network Analysis and Mining

Published version of a preprint

Abstract

The dissemination of disinformation has become a formidable weapon, with nation-states exploiting social media platforms to engineer narratives favorable to their geopolitical interests. This study delved into Russia’s orchestrated disinformation campaign, in three times periods of the 2022 Russian-Ukraine War: its incursion, its midpoint and the Ukrainian Kherson counteroffensive. This period is marked by a sophisticated blend of bot-driven strategies to mold online discourse. Utilizing a dataset derived from Twitter, the research examines how Russia leveraged automated agents to advance its political narrative, shedding light on the global implications of such digital warfare and the swift emergence of counter-narratives to thwart the disinformation campaign. This paper introduces a methodological framework that adopts a multiple-analysis model approach, initially harnessing unsupervised learning techniques, with TweetBERT for topic modeling, to dissect disinformation dissemination within the dataset. Utilizing Moral Foundation Theory and the BEND Framework, this paper dissects social-cyber interactions in maneuver warfare, thereby understanding the evolution of bot tactics employed by Russia and its counterparts within the Russian-Ukraine crisis. The findings highlight the instrumental role of bots in amplifying political narratives and manipulating public opinion, with distinct strategies in narrative and community maneuvers identified through the BEND framework. Moral Foundation Theory reveals how moral justifications were embedded in these narratives, showcasing the complexity of digital propaganda and its impact on public perception and geopolitical dynamics. The study shows how pro-Russian bots were used to foster a narrative of protection and necessity, thereby seeking to legitimize Russia’s actions in Ukraine whilst degrading both NATO and Ukraine’s actions. Simultaneously, the study explores the resilient counter-narratives of pro-Ukraine forces, revealing their strategic use of social media platforms to counteract Russian disinformation, foster global solidarity, and uphold democratic narratives. These efforts highlight the emerging role of social media as a digital battleground for narrative supremacy, where both sides leverage information warfare tactics to sway public opinion.

Overview

Integrity signals

Registry

Crossref · 10.1007/s13278-024-01322-w

Cites

1 of 1 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Verification

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

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?

Citations

Cites

Flagged first, then by citations

Show all 1 references

Research works printed but not linked: 25

  1. Adrian K (2021) Moral and political foundations: from political psychology to political realism. Moral Philos Politics 10(1):139–159
  2. Alieva I, Ng LH, Carley KM (2022) Investigating the spread of Russian disinformation about biolabs in Ukraine on twitter using social network analysis. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 1770–1775. IEEE · DOI
  3. Carley Kathleen M (2020) An emerging science: social cybersecurity. Comput Math Organ Theory 26:365–381 · DOI
  4. Chavoshi N, Hamooni H, Mueen A (2016) Debot: twitter bot detection via warped correlation. Icdm 18:28–65
  5. Chen E, Ferrara, E (2023) Tweets in time of conflict: a public dataset tracking the twitter discourse on the war between Ukraine and Russia. In: Proceedings of the Seventeenth International AAAI Conference on Web and Social Media (ICWSM), · DOI
  6. Claudia O-R, Mark TK (2018) Modeling and predicting news consumption on twitter. In: Proceedings of the 26th Conference on User Modeling, Adaptation and Personalization, pp 321–329, 2018 · DOI
  7. David M Beskow and Kathleen M Carley (2018) Bot-hunter: a tiered approach to detecting & characterizing automated activity on twitter. In: Conference paper. SBP-BRiMS: International conference on social computing, behavioral-cultural modeling and prediction and behavior representation in modeling and simulation, vol. 3
  8. de Keulenaar Emillie, Magalhães João C, Ganesh Bharath (2023) Modulating moderation: a history of objectionability in twitter moderation practices. J Commun 73(3):273–287. https://doi.org/10.1093/joc/jqad015 · DOI
  9. Graham Jesse, Haidt Sena Koleva, Motyl Matt, Iyer Ravi, Wojcik Sean P, Ditto Peter H (2013) Moral foundations theory: the pragmatic validity of moral pluralism. Adv Exp Soc Psychol 47:55–130 · DOI
  10. Hatemi Peter K, Crabtree Charles, Smith Kevin B (2019) Ideology justifies morality: political beliefs predict moral foundations. Am J Political Sci 63(4):788–806 · DOI
  11. Jarred P (2017) Commanding the trend: social media as information warfare. Routledge, London
  12. Judith B, Yicong H, Sydney G, Suellen H, Chen L, Gloria M (2023) Understanding underlying moral values and language use of COVID-19 vaccine attitudes on Twitter — academic.oup.com. https://academic.oup.com/pnasnexus/article/2/3/pgad013/7070624,
  13. Kareem D, Peter S, Michaël A, Nakov P (2023) View of unsupervised user stance detection on twitter — ojs.aaai.org. https://ojs.aaai.org/index.php/ICWSM/article/view/7286/7140,
  14. Kumankov Arseniy (2023) View of Nazism, genocide and the threat of the global west Russian moral justification of war in Ukraine. Etikk i praksis-Nordic J Appl Ethics 1:7–27
  15. Liangjie H, Brian DD (2011) Empirical study of topic modeling in Twitter. In: Proceedings of the First Workshop on Social Media Analytics — dl.acm.org. https://dl.acm.org/doi/pdf/10.1145/1964858.1964870, · DOI
  16. Mohiuddin MAQ, Vijay M (2020) Tweetbert: a pretrained language representation model for twitter text Analysis. https://arxiv.org/pdf/2010.11091.pdf · arXiv
  17. National academies of sciences, engineering, and medicine. Chapter 6: integrating social and behavioral sciences (SBS) Research to Enhance Security in Cyberspace, chapter 6. The National Academies Press, Washington, DC, 2019. https://doi.org/10.17226/25335 · DOI
  18. Ofer F (2019) On the “Gerasimov Doctrine” — jstor.org. https://www.jstor.org/stable/pdf/26803233.pdf
  19. Peixian Z, Ehsan-Ul H, Yiming Z, Pan H, and Gareth T (2023) Echo Chambers within the Russo-Ukrainian War: The Role of Bipartisan Users. In: Proceedings of the International Conference on Advances in Social Networks Analysis and Mining, pp. 154-158 · DOI
  20. Peng T (2023) Differentiation and unity: a cross-platform comparison analysis of online posts’ semantics of the Russian-Ukrainian war based on Weibo and twitter. Commun Public 8(2):105–124 · DOI
  21. Smith Steven T, Kao Edward K, Mackin Erika D, Shah Danelle C, Simek Olga, Rubin Donald B (2021) Automatic detection of influential actors in disinformation networks. Proc Natl Acad Sci 118(4):2011216118 · DOI
  22. Song H, Tolochko P, Eberl J-M, Eisele O, Greussing E, Heidenreich T, Lind F, Galyga S, Boomgaarden HG (2020) In validations we trust? the impact of imperfect human annotations as a gold standard on the quality of validation of automated content analysis. Polit Commun 37(4):553–575. https://doi.org/10.1080/10584609.2020.1723752 · DOI · Paper page
  23. Ng LHX, Robertson DC, Carley KM (2022) Stabilizing a supervised bot detection algorithm: How much data is needed for consistent predictions?. Online Social Networks and Media, 28, 100198. · DOI
  24. Ng LHX, Carley KM (2023a) Botbuster: Multi-platform bot detection using a mixture of experts. In Proceedings of the international AAAI conference on web and social media 17:686–697. · DOI
  25. Ng LHX, Carley KM (2023b) Deflating the Chinese balloon: types of Twitter bots in US-China balloon incident. EPJ Data Science, 12(1):63. · DOI

Cited by

Sorted by citations

See all 32

Metadata

Authors
Marigliano, R., Ng, L. H. X., & Carley, K. M.
Affiliations
Carnegie Mellon University (US)
Corresponding author
Marigliano, Rebecca
Venue
Social Network Analysis and Mining
Published
2024-08-23
Volume
14
Issue
1
ISSN
  • 1869-5450 (ISSN-L)
  • 1869-5469
OpenAlex
W4401805905
Cited by
32
References
1
Citations by year
2026: 17 · 2025: 14 · 2024: 1
FWCI
23.05
Type
journal article
Language
en
Open access
Locations
  • Social Network Analysis and Mining (publishedVersion, cc-by)
  • Research Square (acceptedVersion, cc-by)
Funders
  • U.S. Army (W911NF20D0002)
  • Office of Naval Research (N000142112229)
  • Carnegie Mellon University
Topics
  • Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
  • Social Sciences › Political Science and International Relations › European and Russian Geopolitical Military Strategies
  • Social Sciences › Communication › Social Media and Politics
Keywords
Russian disinformation campaigns, Russian invasion of Ukraine, counter-narratives, information warfare, Twitter, topic modeling, public opinion manipulation, geopolitical narratives

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