Predicting gender from electronic discourse.

There is substantial evidence of gender differences in face-to-face communication, and we suspect that similar differences are present in electronic communication. We designed three studies to examine gender-preferential language style in electronic discourse. In Expt 1, participants sent electronic messages to a designated 'netpal'. A discriminant analysis showed that it was possible to successfully classify the participants by gender with 91.4% accuracy. In Expts 2 and 3, we wanted to determine whether readers of e-mails could accurately identify author gender. We gave participants a selection of messages from Expt 1 and asked them to predict the author's gender. It was found that for 14 of the 16 messages used, the gender of author was correctly predicted. In the third experiment, six messages about gender-neutral topics were composed. Using a subset of the variables identified in Expt 1, female and male versions of each message were created. When participants were asked to rate whether a female or a male wrote these messages, their ratings differed as a function of the message version. These findings establish that people use gender-preferential language in informal electronic discourse. Furthermore, readers of these messages can use these gender-linked language differences to identify the author's gender.