Twitter opens applications for Moderation Research Consortium

Twitter Moderation

As a part of its initiative to uphold data-driven transparency, Twitter is launching the Twitter Moderation Research Consortium (TMRC) — a global group of experts studying platform governance issues and has published a comprehensive public archive of data related to state-backed information operations.

Twitter is offering researchers the opportunity to apply for membership in the Twitter Moderation Research Consortium, and as a combined effort of researchers, it has generated 52 datasets spanning nine terabytes of media and more than 220 Mn Tweets.

How to apply for the Twitter Moderation Research Consortium?

Membership in the TMRC is open to global researchers from across academia, civil society, NGOs, and journalism. Our goal is simple: prioritize transparency by sharing more data on more issues with those who are studying content moderation.

Twitter developed the application process in consultation with members of the Trust & Safety Council and other global experts. Users can learn more about the Consortium here or review eligibility criteria and apply for membership here.

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Why is transparency such a priority on Twitter?

Twitter mentioned that over the years, they learned and shared about the importance of combating manipulation and interference in political conversations on Twitter. Providing academics and researchers with access to specific, granular data (not just aggregated reports), is intended to enable them to find insights and contextualize information in a way that increases the visibility of the reports themselves.

The platform states it would continue to share platform manipulation campaigns and information operations with the Consortium, in line with the platform manipulation and spam policy. This includes sharing data with TMRC researchers about the networks it removes and technical information about the presumptive country of origin of information operations. However, Twitter will no longer share its own attribution information for these datasets.