Abstract

There are several reasons why gender recognition is vital for online social networks such as community Question Answering (cQA) platforms. One of them is progressing towards gender parity across topics as a means of keeping communities vibrant. More specifically, this demographic variable has shown to play a crucial role in devising better user engagement strategies. For instance, by kindling the interest of their members for topics dominated by the opposite gender. However, in most cQA websites, the gender field is neither mandatory nor verified when submitting and processing enrollment forms. And as might be expected, it is left blank most of the time, forcing cQA services to infer this demographic information from the activity of their users on their platforms such as prompted questions, answers, self-descriptions and profile images. There is only a handful of studies dissecting automatic gender recognition across cQA fellows, and as far as we know, this work is the first effort to delve into the contribution of their profile pictures to this task. Since these images are an unconstrained environment, their multifariousness poses a particularly difficult and interesting challenge. With this mind, we assessed the performance of three state-of-art image processing techniques, namely pre-trained neural network models. In a nutshell, our best configuration finished with an accuracy of 81.68% (Inception-ResNet-50), and its corresponding Grad-Cam maps unveil that one of its principal focus of attention is determining silhouettes edges. All in all, we envisage that our findings are going to play a fundamental part in the design of efficient multi-modal strategies.