Women’s Forums on the Dark Web

Abstract

With the recent advent of Web 2.0, more and more women participate in and exchange opinions through community-based social media on the Internet. Questions concerning gender differences in the context of online communication have been raised. In this study, we develop a feature-based text classification framework to examine the online gender differences between female and male posters on web forums by analyzing writing styles and topics of interests. We examine the performance of different feature sets in an experiment involving political opinions. The results of our experimental study on this Islamic women’s political forum show that the feature sets containing both content-free and content-specific features perform significantly better than those consisting of only content-free features. In addition, feature subset selection can improve the classification results significantly. Female and male participants were found to have significantly different topics of interest in our study.