Detecting bipolar disorder on social media by post grouping and interpretable deep learning

Abstract

Bipolar disorder is a disorder in which a person expresses manic and depressed emotions repeatedly. Diagnosing bipolar disorder accurately can be difficult because other mood disorders or even regular mood changes may have similar symptoms. Therefore, psychiatrists need to spend a long time observing and interviewing clients to make the diagnosis. Recent studies have trained machine learning models for detecting bipolar disorder on social media. However, most of these studies focused on increasing the accuracy of the model without explaining the classification results. Although the posts of a bipolar disorder user can be observed manually, doing so is not practical since a user can have many posts which may not depict any signs of bipolar disorder. Without any explanations, the trustworthiness of the model decreases. We propose a deep learning model that not only detects and classifies bipolar disorder users but also explains how the model generates the classification results. The posts are first grouped using Latent Dirichlet Allocation, a method commonly used to classify the topic of a text. These groups are then input into the model, and attention mechanisms are utilized to determine which groups have more attention weights and are considered more heavily. Finally, an explanation of the classification results is obtained by visualizing the attention weights. Several case studies are presented to demonstrate the explanations generated through our proposed model. Our model is also compared to other models, achieving the best performance with an F1-Score of 0.92.