FUBU-EPSTEIN

A large-scale, multi-year Twitter corpus capturing the global public discourse on the Jeffrey Epstein criminal case.

Citation: Michael Kreil, Tristan Manfred Stöber, and Daniel Thilo Schroeder. "FUBU-EPSTEIN: A Large-Scale Twitter Dataset on the Jeffrey Epstein Case and Its Global Public Discourse (2019–2023)." arXiv:2608.10210 [cs.SI], 2026.

Access: Public anonymized release at Zenodo (10.5281/zenodo.21621944); raw data available on request at fubu.dataset@gmail.com.

Overview

Scope: 54.38 million Twitter statuses (4.22M tweets, 42.39M retweets, 6.64M replies, 1.08M quotes, 51k quote-in-reply) authored by 7.11 million users, collected continuously from August 3, 2019 to April 3, 2023.

Annotations: Each unique source text (13.98M unique texts) is labeled with Qwen2.5-7B-Instruct on: - Sentiment: positive, negative, neutral (54.1% negative, 42.8% neutral) - Conspiracy: supports, reports, debunks, or no conspiracy (47.0% report conspiracy, 6.3% support) - Misinformation: supports, reports, debunks, or no misinformation (7.2% support misinformation) - Toxicity: severity levels (mild, moderate, severe) and category scores (hate speech, harassment, threat, profanity) - Moral emotion: condemning, praising, suffering, self-conscious, neutral (50.9% neutral, 46.6% other-condemning) - Ideology & partisanship: detection flags and orientation - Mobilization: call-to-action presence and type - Bot likelihood: likely human, uncertain, likely bot/spam

Additionally, all texts receive independent six-class moral-emotion classification via the Chaeyoon/ELECTRA-Moral-Emotion-ENG classifier.

Graph: Directed social contact graph of 37.03 million edge rows, derived from retweets, replies, quotes, and mentions.

Dataset Structure (Public V4 Release)

Three Apache Parquet tables, ~1.34 GB total:

1. statuses.parquet (54.38M rows): anonymized record and user IDs, chronological order, coarse dates, status type, annotation ID, language
2. annotations.parquet (13.98M rows): Qwen categorical and numeric labels, moral-emotion scores; no source text
3. relations.parquet (46.15M rows): internal retweet, reply, and quote links between release record IDs

Includes aggregate summaries by temporal period, language, label, diffusion, and network properties, plus manifest and validation reports.

Key Characteristics

• Continuous near-real-time collection via Twitter Search API v1.1, deployed on Raspberry Pi 4
• Fixed keyword set (unchanged after August 13, 2019): epstein, epsteingate, epsteinblackbook, clintonbodycount, trumpbodycount, and 10 related terms
• Captures temporal dynamics: activity peaks sharply in August 2019 (9.57M statuses), remains elevated through 2019–2020, then declines through 2021–2023
• Deduplication and chronological sorting: eliminates redundant captures, enables time-series analysis
• Embedded conversation structure: preserves reply threads, retweet chains, and quote contexts
• Anonymization: replaces all original platform identifiers with random integer mappings; excludes tweet text, user handles, exact timestamps, and profiles in public release
• Preprocessing pipeline: custom Java-based five-stage pipeline handles datasets up to 1.9 TB heap; produces compact binary format

Thematic Content

The discourse blends: - Primary-source evidence: flight logs, court dockets, leaked documents, "Maxwell Interview" transcripts from DOJ releases - Conspiratorial framings: "Epstein didn't kill himself" motif, blame attribution to elites and institutions - Attention shocks: renewed investigative reporting, major court filings, post-mortem legal accountability, mandated publication of "Epstein files" (2025–2026)

47.0% of labeled statuses report conspiracy-related claims; 6.3% actively support conspiracy theories; 2.2% explicitly debunk them. Sentiment is predominantly negative (54.1%) or neutral (42.8%), with 46.6% of texts expressing moral outrage or condemnation.

Connections

• Rumor detection on social media and Fake news detection — applies LLM-based annotation to real-world misinformation cascade
• Conspiracy theories — dataset study of how conspiracy narratives form and propagate
• Sentiment Analysis — uses sentiment classification as a primary annotation dimension
• Social Media Analysis — foundational Twitter corpus for studying public discourse dynamics
• Bot detection — includes bot-likelihood scores for user-level analysis
• Computational Propaganda — examines how coordinated or mass-amplified narratives circulate

Related Resources

• DOJ Epstein portal: Primary documents and disclosures at justice.gov/epstein
• Flight logs & legal documents: Available via DocumentCloud and U.S. v. Maxwell exhibits
• Prior Epstein datasets: Schatto-Eckrodt et al. (8-million-post cross-platform corpus, 2024); Attanasio et al. (2.28M Reddit posts from r/conspiracy, 2025)
• Code & readers:
• Preprocessing and collection code: https://github.com/MichaelKreil/twitter-analysis
• Python reader for V3 binary format: https://github.com/DTSchroeder/large-twitter-data-processor

Strengths & Notes

Strengths: - Long temporal coverage (3.75 years) captures the full arc of institutional accountability discourse - Continuous collection preserves deleted/edited posts and profile evolution - Rich multi-dimensional annotations (conspiracy, misinformation, toxicity, moral emotion) enable nuanced analysis - Large social graph (37M edges) supports network-level and cascade analyses - Public anonymized release facilitates reproducible research without platform-compliance friction

Limitations: - Restricted to English-dominant discourse (91.2% of corpus) - Keyword-based collection may miss contextual framing (e.g., implicit references without keywords) - LLM annotations (Qwen2.5-7B-Instruct) can introduce systematic bias; 47.7% raw agreement with independent moral-emotion classifier - Anonymization prevents direct user- or account-level insights; large cascades may retain temporal/structural fingerprints - Public release excludes tweet text, limiting qualitative analysis without requesting raw data

Notes

This dataset serves as a historical baseline for human-mediated misinformation and conspiracy dynamics predating widespread LLM-based synthetic-consensus systems and AI-swarm coordination. It is particularly valuable for studying how accountability narratives form around high-profile criminal cases, how evidence fragments are recontextualized and circulate, and how conspiratorial framings persist despite official document releases and fact-checking.

The multi-year collection window and rich annotation layers make FUBU-EPSTEIN a canonical reference for studying polarized discourse, attention dynamics, and moral-emotional framing in the context of institutional failure and delayed accountability.