Propaganda and Misinformation on Facebook and Twitter during the Russian Invasion of Ukraine
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Abstract (arXiv)
Online social media represent an oftentimes unique source of information, and having access to reliable and unbiased content is crucial, especially during crises and contentious events. We study the spread of propaganda and misinformation that circulated on Facebook and Twitter during the first few months of the Russia-Ukraine conflict. By leveraging two large datasets of millions of social media posts, we estimate the prevalence of Russian propaganda and low-credibility content on the two platforms, describing temporal patterns and highlighting the disproportionate role played by superspreaders in amplifying unreliable content. We infer the political leaning of Facebook pages and Twitter users sharing propaganda and misinformation, and observe they tend to be more right-leaning than the average. By estimating the amount of content moderated by the two platforms, we show that only about 8-15% of the posts and tweets sharing links to Russian propaganda or untrustworthy sources were removed. Overall, our findings show that Facebook and Twitter are still vulnerable to abuse, especially during crises: we highlight the need to urgently address this issue to preserve the integrity of online conversations.
Key facts. Cited by 10 papers in the map (data through 14 Oct 2026). First posted on arXiv 1 Dec 2022.
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Citing papers per year: 2023: 5; 2024: 1; 2025: 1; 2026: 3.
10 papers.
- 2023Advancing Beyond Identification: Multi-bit Watermark for Large Language Modelscited by 57
- 2023Social-LLM: Modeling User Behavior at Scale using Language Models and Social Network Datacited by 11
- 2023Analyzing the Strategy of Propaganda using Inverse Reinforcement Learning: Evidence from the 2022 Russian Invasion of Ukrainecited by 2
- 2023Unveiling Global Narratives: A Multilingual Twitter Dataset of News Media on the Russo-Ukrainian Conflictcited by 1
- 2026An evaluation of LLMs for political bias in Western media: Israel-Hamas and Ukraine-Russia warscited by 1
- 2026Addressing Data Scarcity in Bangla Fake News Detection: An LLM-Based Dataset Augmentation Approachcited by 1
- 2023Modeling Political Orientation of Social Media Posts: An Extended Analysiscited by 0
- 2024Exploring the topics, sentiments and hate speech in the Spanish information environmentcited by 0
- 2025SemCAFE: When Named Entities make the Difference Assessing Web Source Reliability through Entity-level Analyticscited by 0
- 2026Large-Scale Analysis of Persuasive Content on Moltbookcited by 0
- 2026Addressing Data Scarcity in Bangla Fake News Detection: An LLM-Based Dataset Augmentation Approach2 May 2026
- 2026Large-Scale Analysis of Persuasive Content on Moltbook18 Mar 2026
- 2026An evaluation of LLMs for political bias in Western media: Israel-Hamas and Ukraine-Russia wars5 Jan 2026
- 2025SemCAFE: When Named Entities make the Difference Assessing Web Source Reliability through Entity-level Analytics3 Apr 2025
- 2024Exploring the topics, sentiments and hate speech in the Spanish information environment19 Sep 2024
- 2023Social-LLM: Modeling User Behavior at Scale using Language Models and Social Network Data31 Dec 2023
- 2023Modeling Political Orientation of Social Media Posts: An Extended Analysis21 Nov 2023
- 2023Advancing Beyond Identification: Multi-bit Watermark for Large Language Models1 Aug 2023
- 2023Analyzing the Strategy of Propaganda using Inverse Reinforcement Learning: Evidence from the 2022 Russian Invasion of Ukraine24 Jul 2023
- 2023Unveiling Global Narratives: A Multilingual Twitter Dataset of News Media on the Russo-Ukrainian Conflict22 Jun 2023