A Public Dataset Tracking Social Media Discourse about the 2024 U.S. Presidential Election on Twitter/X
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Abstract (arXiv)
In this paper, we introduce the first release of a large-scale dataset capturing discourse on mathbbX (a.k.a., Twitter) related to the upcoming 2024 U.S. Presidential Election. Our dataset comprises 22 million publicly available posts on X.com, collected from May 1, 2024, to July 31, 2024, using a custom-built scraper, which we describe in detail. By employing targeted keywords linked to key political figures, events, and emerging issues, we aligned data collection with the election cycle to capture evolving public sentiment and the dynamics of political engagement on social media. This dataset offers researchers a robust foundation to investigate critical questions about the influence of social media in shaping political discourse, the propagation of election-related narratives, and the spread of misinformation. We also present a preliminary analysis that highlights prominent hashtags and keywords within the dataset, offering initial insights into the dominant themes and conversations occurring in the lead-up to the election. Our dataset is available at: urlhttps://github.com/sinking8/usc-x-24-us-election
Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). First posted on arXiv 1 Nov 2024.
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Citing papers per year: 2025: 3; 2026: 4.
7 papers.
- 2025Generative Exaggeration in LLM Social Agents: Consistency, Bias, and Toxicitycited by 8
- 2025Beyond speculation: Measuring the growing presence of LLM-generated texts in multilingual disinformationcited by 1
- 2025Community-Aligned Behavior Under Uncertainty: Evidence of Epistemic Stance Transfer in LLMscited by 0
- 2026Mapping Election Toxicity on Social Media across Issue, Ideology, and Psychosocial Dimensionscited by 0
- 2026The Algorithmic Caricature: Auditing LLM-Generated Political Discourse Across Crisis Eventscited by 0
- 2026Making Political Text Scaling Comparable: Infrastructure and Hyperparameter Sensitivity for 17 Algorithmscited by 0
- 2026Why Does Misinformation Propagate Faster? An Algorithmic Perspective on Xcited by 0
- 2026Why Does Misinformation Propagate Faster? An Algorithmic Perspective on X24 Sep 2026
- 2026Making Political Text Scaling Comparable: Infrastructure and Hyperparameter Sensitivity for 17 Algorithms13 Sep 2026
- 2026The Algorithmic Caricature: Auditing LLM-Generated Political Discourse Across Crisis Events12 May 2026
- 2026Mapping Election Toxicity on Social Media across Issue, Ideology, and Psychosocial Dimensions18 Apr 2026
- 2025Community-Aligned Behavior Under Uncertainty: Evidence of Epistemic Stance Transfer in LLMs14 Nov 2025
- 2025Generative Exaggeration in LLM Social Agents: Consistency, Bias, and Toxicity1 Jul 2025
- 2025Beyond speculation: Measuring the growing presence of LLM-generated texts in multilingual disinformation29 Mar 2025