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Discord Unveiled: A Comprehensive Dataset of Public Communication (2015-2024)

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arxiv 2502.00627 v1 pith:KUGICGC7 submitted 2025-02-02 cs.SI cs.DB

Discord Unveiled: A Comprehensive Dataset of Public Communication (2015-2024)

classification cs.SI cs.DB
keywords discordpublicdatasetcommunicationdataserverssocialcommunity
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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Discord has evolved from a gaming-focused communication tool into a versatile platform supporting diverse online communities. Despite its large user base and active public servers, academic research on Discord remains limited due to data accessibility challenges. This paper introduces Discord Unveiled: A Comprehensive Dataset of Public Communication (2015-2024), the most extensive Discord public server's data to date. The dataset comprises over 2.05 billion messages from 4.74 million users across 3,167 public servers, representing approximately 10% of servers listed in Discord's Discovery feature. Spanning from Discord's launch in 2015 to the end of 2024, it offers a robust temporal and thematic framework for analyzing decentralized moderation, community governance, information dissemination, and social dynamics. Data was collected through Discord's public API, adhering to ethical guidelines and privacy standards via anonymization techniques. Organized into structured JSON files, the dataset facilitates seamless integration with computational social science methodologies. Preliminary analyses reveal significant trends in user engagement, bot utilization, and linguistic diversity, with English predominating alongside substantial representations of Spanish, French, and Portuguese. Additionally, prevalent community themes such as social, art, music, and memes highlight Discord's expansion beyond its gaming origins.

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Cited by 1 Pith paper

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  1. Understanding Interpretation Difficulty in Harmful Online Communication: Insights from Cybercrime Communities

    cs.CL 2026-07 conditional novelty 5.0

    Interpreting harmful Discord messages requires integrating external knowledge and extended context, not just local message-level classification; LLMs leverage local context better than humans but still fail on coded l...