Tweet Author Gender Identification. PAN 2016 Task

Abstract

The paper presents an experiment of tweet’s author gender detection. We used PAN 2016 data and task description and have build an application that decides whether an analysed tweet has been written by man or woman. Multiple texts’ characteristics are used as features in the application, such as: references to pictures, to web pages, to other people, emojis, hashtags and a number of words that are associated with tweets written by women and men respectively. For 100 random tweets we obtained average accuracy 0.61. This is good result although it is not as good as the best one in PAN 2016 task.