Gender classification of twitter data based on textual meta-attributes extraction

Abstract

With the growth of social media in recent years, there has been an increasing interest in the automatic characterization of users based on the informal content they generate. In this context, the labeling of users in demographic categories, such as age, ethnicity, origin and race, among the investigation of other attributes inherent to users, such as political preferences, personality and gender expression, has received a great deal of attention, especially based on Twitter data. The present paper focuses on the task of gender classification by using 60 textual meta-attributes, commonly used on text attribution tasks, for the extraction of gender expression linguistic cues in tweets written in Portuguese. Therefore, taking into account characters, syntax, words, structure and morphology of short length, multi-genre, content free texts posted on Twitter to classify author's gender via three different machine-learning algorithms as well as evaluate the influence of the proposed meta-attributes in this process.