Motivated with recent advances in inferring users' mental state in social media posts, we identify and formulate the problem of finding causal indicators behind mental illness in self-reported text. In the past, we witness the presence of rule-based studies for causal explanation analysis on curated Facebook data. The investigation on transformer-based model for multi-class causal categorization in Reddit posts point to a problem of using long-text which contains as many as 4000 words. Developing end-to-end transformer-based models subject to the limitation of maximum-length in a given instance. To handle this problem, we use Longformer and deploy its encoding on transformer-based classifier. The experimental results show that Longformer achieves new state-of-the-art results on M-CAMS, a publicly available dataset with 62\% F1-score. Cause-specific analysis and ablation study prove the effectiveness of Longformer. We believe our work facilitates causal analysis of depression and suicide risk on social media data, and shows potential for application on other mental health conditions.

使用 Longformer 对社交媒体数据中的自述文本进行编码，从而发现与心理疾病的因果关系，取得了最新的 M-CAMS 数据集上的最佳结果，为分析抑郁症和自杀风险提供了有效途径。

长文本中心理困扰原因的多类别分类