Multi-Platform Aggregated Dataset of Online Communities (MADOC)
Marija Mitrović Dankulov, Aleksandar Tomašević, Slobodan Maletić, Miroslav Anđelković, Ana Vranić, Darja Cvetković, Boris Stupovski, Dušan Vudragović, Sara Major, Aleksandar Bogojević
TL;DR
MADOC addresses the need for a cross-platform, FAIR-compliant dataset to study online communities across multiple platforms in the post-API era. The authors aggregate data from Reddit, Voat, Bluesky, and Koo, standardize interactions into a common Parquet-based schema, apply LDA-based content alignment, sentiment analysis, bot filtering, and UUID pseudonymization. The dataset includes 18.9 million posts, 236 million comments, and 23.1 million users, spanning 2013–2024, with detailed platform- and community-level statistics and analytics-ready metadata. MADOC enables cross-platform analyses of moderation effects, community dynamics, and toxic-content diffusion, and provides Python/R tooling to facilitate adoption, while highlighting ethical considerations and future directions.
Abstract
The Multi-platform Aggregated Dataset of Online Communities (MADOC) is a comprehensive dataset that facilitates computational social science research by providing FAIR-compliant standardized access to cross-platform analysis of online social dynamics. MADOC aggregates and standardizes data from Bluesky, Koo, Reddit, and Voat (2012-2024), containing 18.9 million posts, 236 million comments, and 23.1 million unique users. The dataset enables comparative studies of toxic behavior evolution across platforms through standardized interaction records and sentiment analysis. By providing UUID-anonymized user histories and temporal alignment of banned communities' activity patterns, MADOC supports research on content moderation impacts and platform migration trends. Distributed via Zenodo with persistent identifiers and Python/R toolkits, the dataset adheres to FAIR principles while addressing post-API-era research challenges through ethical aggregation of public social media archives.