From Birdwatch to Community Notes, from Twitter to X - arXiv

From Birdwatch to Community Notes, from Twitter to X:
 four years of community-based content moderation
 

 
 
 Saeedeh Mohammadi
 
 School of Mathematics and Statistics, University College Dublin, Dublin, Ireland
 
 
 
 
 Narges Chinichian
 
 Institute for Theoretical Physics, Technical University of Berlin, Berlin, Germany
 
 SPICED Academy, Berlin, Germany
 
 These authors contributed equally
 
 
 Hannah Doyal
 
 SPICED Academy, Berlin, Germany
 
 These authors contributed equally
 
 
 Kristina Skutilova
 
 School of Computer Science, University College Dublin, Dublin, Ireland
 
 
 Hao Cui
 
 School of Social Sciences and Philosophy, Trinity College Dublin, Dublin, Ireland
 
 
 Michele d’Errico
 
 School of Social Sciences and Philosophy, Trinity College Dublin, Dublin, Ireland
 
 
 Siobhan Grayson
 
 School of Sociology, University College Dublin, Dublin, Ireland
 
 
 Taha Yasseri
 
 School of Mathematics and Statistics, University College Dublin, Dublin, Ireland
 
 School of Social Sciences and Philosophy, Trinity College Dublin, Dublin, Ireland
 
 Faculty of Arts and Humanities, Technological University Dublin, Dublin, Ireland
 
 
 
 
 
 SUMMARY

 
 Community Notes (formerly known as Birdwatch) is the first large-scale crowdsourced content moderation initiative that was launched by X (formerly known as Twitter) in January 2021. As the Community Notes model gains momentum across other social media platforms, there is a growing need to assess its underlying dynamics and effectiveness. This Resource paper provides (a) a systematic review of the literature on Community Notes, and (b) a major curated dataset and accompanying source code to support future research on Community Notes. We parsed Notes and Ratings data from the first four years of the program and conducted language detection across all Notes. Focusing on English-language Notes, we extracted embedded URLs and identified discussion topics in each Note. Additionally, we constructed monthly interaction networks among the Contributors. Together with the literature review, these resources offer a robust foundation for advancing research on the Community Notes system.

 
 
 
 KEYWORDS

 
 Content Moderation, Community Notes, Birdwatch, Network Analysis, Topic Modelling

 
 
 
 INTRODUCTION

 
 The rapid production and spread of user-generated content on social media platforms in the absence of editorial oversight has increased users’ exposure to false or misleading information 1 . In response, social media platforms have taken various approaches to content moderation. Content Moderation refers to the process of monitoring, flagging, or removing content that violates community guidelines or is deemed harmful. The main moderation strategies have been expert review and algorithmic systems. However, each comes with limitations. Expert evaluation, while often accurate, is costly and impractical at scale, given the sheer volume of content that needs moderation 2 . Automated algorithmic methods, 

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