A Weighted Combination of Text and Image Classifiers for User Gender Inference

Demographic attribute inference of social networking service (SNS) users is a valuable application for marketing and for targeting advertisements. Several studies have examined Twitter-user gender inference in natural language processing, image recognition, and other research domains. Reportedly, a combined approach using text data and image data outperforms an individual data approach. This paper presents a proposal of a novel hybrid approach. A salient benefit of our system is that features provided from a text classifier and from an image classifier are combined appropriately to infer male or female gender using logistic regression. The experimentally obtained results demonstrate that our approach markedly improves an existing combination-based method.