A Multi-View Learning Approach for Detecting Personality Disorders Among Arab Social Media Users

Abstract

Multi-view fusion approaches have gained increasing interest in the past few years by researchers. This interest comes due to the many perspectives that datasets can be looked at and evaluated. One of the most urging areas that require constant leveraging with latest technologies and multi-perspective judgments is the area of psychology. In this article, a novel multi-view fusion model using deep learning algorithms is presented to detect popular types of personality disorders among Arab users of the Twitter platform in an expert-driven fashion, based on the descriptions of the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition. To the best of our knowledge, the work presented is the first of its kind with no publicly available datasets that report statements around personality disorders in the Arabic language, and thus we created AraPerson, a dataset that consists of 8,000 textual tweets coupled with 8,000 images that prescribe mental statuses for a total of 150 users collected with regular expressions generated under the supervision of domain experts. The dataset was fed into a baseline multi-view model by combining a CNN model with a Bi-LSTM model to detect two types of popular personality disorders by analyzing textual and visual posts on 150 user profiles. The experiments were followed with fusing the DenseNet model with the Bi-LSTM model, experimenting with the effect of using concatenation, addition, and multiplication methods for vectors’ combination. The work presented in this article is unprecedented, specifically in a controversial area such as personality disorders detection among Arab communities. The best reported accuracy is 87%, which is quite promising, as the two types of personality disorders investigated are highly overlapping.