現今社群媒體已成為使用者揭露自我心理狀態的常見場所。許多分析躁鬱症識別的研究將重點放於結合社群媒體和語言特徵來訓練患者識別模型。相反的，此研究主要強調語言特徵，考慮到可能更適用於實際情況，而使用者在社群媒體上的社交行為特徵是比較不容易獲得的。性別也被認為是研究精神疾病的重要特徵；然而，目前只有相當少數關於躁鬱症的研究針對語言使用與性別之間的關係進行分析。我們提出了一種基於純語言句法模式的方法，通過使用者在社群媒體上自我揭露的概況描述進行躁鬱症的識別，其中語言模型是獨立建構的，以保持性別差異。此方法不僅表現優於多個現有的方法，而且還提供語意上的表述，有助於說明躁鬱症與非躁鬱症社群媒體使用者之間情緒行為的差異。透過實驗的執行與結果表明，此研究提出的方法在缺乏語言資源或社群媒體特徵的情況是有效的，有別於過去的研究較注重於社群媒體特徵的分析。

Social media platforms have become popular places for online self-disclosure. Many studies that analyze a digital diagnosis of bipolar disorder focus on combining social media and linguistic features to train a patient recognizing model. In contrast, this current study primarily emphasizes linguistic features, considering that this may be more applicable in the real-world, whereas social media features may not be easily acquired. Gender is also considered to be an important characteristic for mental disorders; however, a limited number of studies about bipolar disorder have analyzed the interactions between language usage and gender. We present an approach that is based on pure linguistic syntactic patterns to perform bipolar disorder recognition from self-reported social media profiles.

摘要
目錄
1 Introduction ----------------------------------------- 1
2 Related Work --------------------------------------- 4
2.1 Social Media and Mental Disorders ---------------- 4
2.2 Gender Gaps in Mental Health -------------------- 7
3 Methodology --------------------------------------- 9
3.1 Overview ----------------------------------------- 9
3.2 Data Collection ----------------------------------- 10
3.3 Linguistic Pattern Construction ------------------- 14
3.3.1 Graph Construction ----------------------------- 14
3.3.2 Pattern Construction ---------------------------- 16
3.3.3 Pattern Weighting ------------------------------- 19
4 Experiment & Results ------------------------------- 22
4.1 Experimental Setup ------------------------------- 22
4.2 Linguistic Patterns Analysis ----------------------- 24
4.3 Emotion Analysis --------------------------------- 26
4.4 Recognition Performance ------------------------- 28
4.4.1 Gender-separated Case ------------------------- 29
4.4.2 Gender-mixed Case ----------------------------- 32
4.4.3 Language Representation Comparison ---------- 33
4.4.4 Psychiatrist Evaluation -------------------------- 35
4.5 Analysis for Low Performance for Male ------------ 36
5 Conclusion and Future Works ----------------------- 38
Reference -------------------------------------------- 40

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