Inferring gender of movie reviewers

Despite differences in the way that men and women experience goods and communicate their perspectives, online review communities typically do not provide participants' gender. We propose to infer author gender, given a set of reviews of a particular item, and experiment on reviews posted at the Internet Movie Database (IMDb). Using logistic regression, we explore the contribution of three types of information: 1) style, 2) content, and 3) metadata (e.g. review age, social feedback). Our results concur with previous research, in that there are salient differences in writing style and content between reviews authored by men versus women. However, in comparison to literary or scientific texts, to which classification tasks are often applied, reviews are brief and occur within the context of an ongoing discourse. Therefore, to compensative for the brevity of reviews, content and stylistic features can be augmented with metadata. We find in particular that the perceived utility of a review is an important correlate of gender. The model incorporating all features has a classification accuracy of 73.7% and is not as sensitive to review length as are those based only on stylistic or content features.