Language Disparities in Moderation Workforce Allocation by Social Media Platforms

Content moderation is one of the earliest and most consequential large-scale applications of artificial intelligence, shaping both online safety and the boundaries of permissible speech. In practice, moderation operates as a sociotechnical human-AI system, combining automated flagging with human review, as automated systems remain too error-prone to operate independently. Leveraging newly mandated transparency disclosures under the European Union's Digital Services Act (DSA), we conduct the first cross-platform audit of human content moderation workforce allocation across languages. Across six major platforms, we uncover substantial cross-lingual disparities in both language coverage and moderator staffing relative to the volume of user-generated content. While larger platforms such as YouTube and Meta employ moderators for many languages, millions of EU-based users on smaller platforms, including Twitter/X, post in languages without any dedicated human oversight. Among covered languages, staffing is often highly disproportionate to content volume, with English consistently prioritized over widely spoken Global Majority languages suchas Spanish, Portuguese, and Arabic. Where enforcement data permit analysis, we further document large cross-lingual disparities in individual moderator workload, with per-moderator daily decision volumes differing by more than an order of magnitude across languages. Together, these findings raise fundamental fairness concerns for both users and moderators, implying both an unequal user protection from online harms across linguistic communities, and an uneven distribution of the cognitive and emotional burdens of moderation labor. They further demonstrate that meaningful accountability for AI-assisted content moderation requires transparency not only about automated systems and enforcement outcomes, but about how human moderation capacity is allocated, justified, and sustained across languages.