Gender Attribution: Tracing Stylometric Evidence Beyond Topic and Genre

Abstract

Sociolinguistic theories (e.g., Lakoff (1973)) postulate that women’s language styles differ from that of men. In this paper, we explore statistical techniques that can learn to identify the gender of authors in modern English text, such as web blogs and scientific papers. Although recent work has shown the efficacy of statistical approaches to gender attribution, we conjecture that the reported performance might be overly optimistic due to non-stylistic factors such as topic bias in gender that can make the gender detection task easier. Our work is the first that consciously avoids gender bias in topics, thereby providing stronger evidence to gender-specific styles in language beyond topic. In addition, our comparative study provides new insights into robustness of various stylometric techniques across topic and genre. 1