METHOD FOR LANGUAGE-INDEPENDENT GENDER CLASSIFICATION ON TWITTER
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May 31, 2016
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May 31, 2016
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Abstract
Online Social Networks (OSNs) allow users to share knowledge, opinions, interests, activities, relationships and friendships with each other. Gender classification of users of an OSN such as Twitter may be difficult to ascertain because gender is not necessarily provided. The present invention relates to a computer-implemented method for predicting gender classification of users of an OSN such as Twitter. The computer-implemented method may predict gender using five color-based features extracted from Twitter profiles such as the background color in a user's profile page. This is in contrast with most existing methods for gender prediction that are language dependent. Those methods use high-dimensional spaces consisting of unique words extracted from such text fields as postings, user names, and profile descriptions. The present method is independent of the user's language, efficient, scalable, and computationally tractable, while attaining a good level of accuracy.
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