Bimodal Gender Classification Across Community Question-Answering Platforms

Community Question-Answering (cQA) sites have an urgent need to be increasingly efficient at (a) offering contextualized/personalized content and (b) linking open questions to people willing to answer. Most recent ideas with respect to attaining this goal combine demographic factors (i.e., gender) with deep neural networks. In essence, recent studies have shown that high gender classification rates are perfectly viable by independently modeling profile images or textual interactions. This paper advances this body of knowledge by leveraging bimodal transformers that fuse gender signals from text and images. Qualitative results suggest that (a) profile avatars reinforce one of the genders manifested across textual inputs, (b) their positive contribution grows in tandem with the number of community fellows that provide this picture, and (c) their use might be detrimental if the goal is distinguishing throwaway/fake profiles. From a quantitative standpoint, ViLT proved to be a better alternative when coping with sparse datasets such as Stack Exchange, whereas CLIP and FLAVA excel with a large-scale collection—namely, Yahoo! answers and Reddit.