Tweets in Time of Conflict: A Public Dataset Tracking the Twitter Discourse on the War Between Ukraine and Russia
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Abstract (arXiv)
On February 24, 2022, Russia invaded Ukraine. In the days that followed, reports kept flooding in from layman to news anchors of a conflict quickly escalating into war. Russia faced immediate backlash and condemnation from the world at large. While the war continues to contribute to an ongoing humanitarian and refugee crisis in Ukraine, a second battlefield has emerged in the online space, both in the use of social media to garner support for both sides of the conflict and also in the context of information warfare. In this paper, we present a collection of over 63 million tweets, from February 22, 2022 through March 8, 2022 that we are publishing for the wider research community to use. This dataset can be found at https://github.com/echen102/ukraine-russia and will be maintained and regularly updated as the war continues to unfold. Our preliminary analysis already shows evidence of public engagement with Russian state sponsored media and other domains that are known to push unreliable information; the former saw a spike in activity on the day of the Russian invasion. Our hope is that this public dataset can help the research community to further understand the ever evolving role that social media plays in information dissemination, influence campaigns, grassroots mobilization, and much more, during a time of conflict.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). First posted on arXiv 14 Mar 2022.
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Citing papers per year: 2022: 1; 2023: 3; 2024: 0; 2025: 2.
6 papers.
- 2023Social-LLM: Modeling User Behavior at Scale using Language Models and Social Network Datacited by 11
- 2022Challenges and Opportunities in Information Manipulation Detection: An Examination of Wartime Russian Mediacited by 10
- 2023Unveiling Global Narratives: A Multilingual Twitter Dataset of News Media on the Russo-Ukrainian Conflictcited by 1
- 2023Modularity-based approach for tracking communities in dynamic social networkscited by 0
- 2025SCRAG: Social Computing-Based Retrieval Augmented Generation for Community Response Forecasting in Social Media Environmentscited by 0
- 2025Involvement drives complexity of language in online debatescited by 0
- 2025Involvement drives complexity of language in online debates27 Jun 2025
- 2025SCRAG: Social Computing-Based Retrieval Augmented Generation for Community Response Forecasting in Social Media Environments18 Apr 2025
- 2023Social-LLM: Modeling User Behavior at Scale using Language Models and Social Network Data31 Dec 2023
- 2023Unveiling Global Narratives: A Multilingual Twitter Dataset of News Media on the Russo-Ukrainian Conflict22 Jun 2023
- 2023Modularity-based approach for tracking communities in dynamic social networks24 Feb 2023
- 2022Challenges and Opportunities in Information Manipulation Detection: An Examination of Wartime Russian Media24 May 2022