Author Gender Prediction in an Email Stream Using Neural Networks

TLDR

The rapid growth of the Internet has made user analysis increasingly important, and authorship analysis can reveal attributes such as gender by exploiting known linguistic differences between male and female writers. The study aims to automatically discriminate gender in Enron emails by leveraging stylometric and word‑count features. The authors employ the Modified Balanced Winnow neural network, an enhanced version of Littlestone’s Balanced Winnow, to process the stylometric and word‑count features. Experiments demonstrate that the Modified Balanced Winnow accurately discriminates gender, with word‑count features outperforming stylometric features.

Abstract

With the rapid growth of the Internet in recent years, the ability to analyze and identify its users has become increasingly important. Authorship analysis provides a means to glean information about the author of a document originating from the internet or elsewhere, including but not limited to the author’s gender. There are well-known linguistic differences between the writing of men and women, and these differences can be effectively used to predict the gender of a document’s author. Capitalizing on these linguistic nuances, this study uses a set of stylometric features and a set of word count features to facilitate automatic gender discrimination on emails from the popular Enron email dataset. These features are used in conjunction with the Modified Balanced Winnow Neural Network proposed by Carvalho and Cohen, an improvement on the original Balanced Winnow created by Littlestone. Experiments with the Modified Balanced Winnow show that it is effectively able to discriminate gender using both stylometric and word count features, with the word count features providing superior results.