  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.
