Gender Classification for Web Forums

More and more women are participating in and exchanging opinions through community-based online social media. Questions concerning gender differences in the new media have been raised. This paper proposes a feature-based text classification framework to examine online gender differences between Web forum posters by analyzing writing styles and topics of interest. Our experiment on an Islamic women´s political forum shows that feature sets containing both content-free and content-specific features perform significantly better than those consisting of only content-free features, feature selection can improve the classification results significantly, and female and male participants have significantly different topics of interest.