In today’s digital age, social media serves as a cornerstone for democratic participation by swiftly disseminating political opinions and information. Paradoxically, this democratizing force harbors a dark side—the proliferation of mysogyny, xenophobia, and false information, often spread under the cloak of anonymity. Anonymity, however, acts as a double-edged sword, enabling free expression while hindering accountability and concealing senders’ motives. Our study combines text analysis with an event study design to explore how anonymity shapes mysogyny, xenophobia, and false information in online political discussions. Employing supervised machine learning models, we quantitatively measure xenophobia and mysogyny. A pivotal event on an online platform, reducing anonymity within a well-defined user group, forms the basis for our difference-in-difference analysis. Our findings uncover the intricate effects of anonymity reduction on online discourse. Decreased anonymity is associated with a clear reduction in xenophobia but no significant change in mysogyny. Interestingly, in discussions about immigration, fake news links decrease when anonymity diminishes, in contrast to discussions about feminism. This research advances our comprehension of the interplay between mysogyny and xenophobia in today’s digitalized world, illuminating the complex dynamics of accountability, discourse, and digital democracy.