Leveraging Linguistic Characteristics for Bipolar Disorder Recognition with Gender Differences
This work addresses the problem of improving mental health monitoring for bipolar disorder patients by incorporating gender differences, though it is incremental as it builds on existing linguistic approaches.
The study tackled bipolar disorder recognition by using only language-based features like syntax and morpheme collocation, achieving over 91% F1 scores and outperforming baselines including TF-IDF, LIWC, and pre-trained models like ELMO and BERT.
Most previous studies on automatic recognition model for bipolar disorder (BD) were based on both social media and linguistic features. The present study investigates the possibility of adopting only language-based features, namely the syntax and morpheme collocation. We also examine the effect of gender on the results considering gender has long been recognized as an important modulating factor for mental disorders, yet it received little attention in previous linguistic models. The present study collects Twitter posts 3 months prior to the self-disclosure by 349 BD users (231 female, 118 male). We construct a set of syntactic patterns in terms of the word usage based on graph pattern construction and pattern attention mechanism. The factors examined are gender differences, syntactic patterns, and bipolar recognition performance. The performance indicates our F1 scores reach over 91% and outperform several baselines, including those using TF-IDF, LIWC and pre-trained language models (ELMO and BERT). The contributions of the present study are: (1) The features are contextualized, domain-agnostic, and purely linguistic. (2) The performance of BD recognition is improved by gender-enriched linguistic pattern features, which are constructed with gender differences in language usage.