Inferring Users’ Gender from Interests: A Tag Embedding Approach

Abstract

This paper studies the problem of gender prediction of users in social media using their interest tags. The challenge is that the tag feature vector is extremely sparse and short, i.e., less than 10 tags for each user. We present a novel conceptual class based method which enriches and centralizes the feature space. We first identify the discriminating tags based on the tag distribution. We then build the initial conceptual class by taking the advantage of the generalization and specification operations on these tags. For example, “Kobe” is a specialized instance of “basketball”. Finally, we model class expansion as a problem of computing the similarity between one tag and a set of tags in one conceptual class in the embedding space.

We conduct extensive experiments on a real dataset from Sina Weibo. Results demonstrate that our proposed method significantly enhances the quality of the feature space and improves the performance of gender classification. Its accuracy reaches 82.25 % while that for the original tag vector is only 62.75 %.