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The vast availability of textual data on social media has led to an interest in algorithms to predict user attributes such as gender based on the user’s writing. These methods are valuable for social science research as well as targeted advertising and profiling, but also compromise the privacy of users who may not realize that their personal idiolects can give away their demographic identities. Can we automatically modify a text so that the author is classified as a certain target gender, under limited knowledge of the classifier, while preserving the text’s fluency and meaning? We present a basic model to modify a text using lexical substitution, show empirical results with Twitter and Yelp data, and outline ideas for extensions.

Obfuscating Gender in Social Media Writing