FUBU-CAPITOL: A Large-Scale Twitter Dataset on the U.S. Capitol Attack and Stop the Steal Discourse (2020–2023)

The January 6, 2021 attack on the United States Capitol emerged from a broader online discourse around election denial, Stop the Steal mobilization, and contested interpretations of political violence. To facilitate computational study of these dynamics, we introduce the FUBU-CAPITOL dataset, a large-scale, multi-dimensional research corpus of 69.42 million Twitter statuses authored by 6.65 million users and collected between December 2020 and April 2023. The source corpus was captured continuously in near real time, preserving statuses before they were deleted, edited, or removed by moderation, and is enriched with a directed social contact graph of 39.48 million edge rows and annotations from Qwen2.5-7B-Instruct covering sentiment, conspiracy and misinformation stance, toxicity, moral emotion, ideology, partisanship, mobilization, and related dimensions. From this source corpus we derived a textless, de-identified public release that retains one row for every deduplicated status, 60.29 million internal status relationships, categorical and numeric annotations, and coarse temporal information, while excluding tweet text, original post and user identifiers, handles, profile fields, exact timestamps, reverse mappings, and the randomization seed. This design allows the corpus to be shared under current data-protection and platform-access constraints without redistributing platform content. To request access to raw data for collaborative research under ethical and legal safeguards, contact [email protected].

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Publication Details

Published
2026-10-15
DOI
https://doi.org/10.31235/osf.io/zf2pc_v1
Primary Topic
Misinformation and Its Impacts
Type
preprint
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preprint

FUBU-CAPITOL: A Large-Scale Twitter Dataset on the U.S. Capitol Attack and Stop the Steal Discourse (2020–2023)

Daniel Thilo Schroeder, Michael Kreil, Tristan M. Stöber
Misinformation and Its Impacts
preprint

FUBU-CAPITOL: A Large-Scale Twitter Dataset on the U.S. Capitol Attack and Stop the Steal Discourse (2020–2023)

Daniel Thilo Schroeder, Michael Kreil, Tristan M. Stöber
preprint en

Abstract

The January 6, 2021 attack on the United States Capitol emerged from a broader online discourse around election denial, Stop the Steal mobilization, and contested interpretations of political violence. To facilitate computational study of these dynamics, we introduce the FUBU-CAPITOL dataset, a large-scale, multi-dimensional research corpus of 69.42 million Twitter statuses authored by 6.65 million users and collected between December 2020 and April 2023. The source corpus was captured continuously in near real time, preserving statuses before they were deleted, edited, or removed by moderation, and is enriched with a directed social contact graph of 39.48 million edge rows and annotations from Qwen2.5-7B-Instruct covering sentiment, conspiracy and misinformation stance, toxicity, moral emotion, ideology, partisanship, mobilization, and related dimensions. From this source corpus we derived a textless, de-identified public release that retains one row for every deduplicated status, 60.29 million internal status relationships, categorical and numeric annotations, and coarse temporal information, while excluding tweet text, original post and user identifiers, handles, profile fields, exact timestamps, reverse mappings, and the randomization seed. This design allows the corpus to be shared under current data-protection and platform-access constraints without redistributing platform content. To request access to raw data for collaborative research under ethical and legal safeguards, contact [email protected].

Peace, justice, and strong institutions
Misinformation and Its Impacts
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