(2002), Mukherjee and Liu (2010)), we conjecture that the reported performance might be overly optimistic under scrutiny due to non-stylistic factors such as topic bias in gender that can make the gender detection task easier.
token-based language models are likely to learn topical words and phrases, and suffer when the topics change dramatically between training and testing.