Interactions among humans on social media often convey intentions behind
their actions, yielding a psychological language resource for Mental Health
Analysis (MHA) of online users. The success of Computational Intelligence
Techniques (CIT) for inferring mental illness from such social media resources
points to NLP as a lens for causal analysis and perception mining. However, we
argue that more consequential and explainable research is required for optimal
impact on clinical psychology practice and personalized mental healthcare. To
bridge this gap, we posit two significant dimensions: (1) Causal analysis to
illustrate a cause and effect relationship in the user generated text; (2)
Perception mining to infer psychological perspectives of social effects on
online users intentions. Within the scope of Natural Language Processing (NLP),
we further explore critical areas of inquiry associated with these two
dimensions, specifically through recent advancements in discourse analysis.
This position paper guides the community to explore solutions in this space and
advance the state of practice in developing conversational agents for inferring
mental health from social media. We advocate for a more explainable approach
toward modeling computational psychology problems through the lens of language
as we observe an increased number of research contributions in dataset and
problem formulation for causal relation extraction and perception enhancements
while inferring mental states.

聊天软件等社交媒体的人际互动对个人心理状况具有暗示作用，而计算智能技术、自然语言处理等方面的持续发展为探讨导致用户在线上状况的原因与心理效应提供了一种全新的视角和工具。本文旨在进一步探讨其中两大关键方向：寻找因果关系与推理发生在用户意识中影响的各种心理角度，在自然语言处理的范畴内通过最新的话语分析技术拓展思路空间，以发掘心理计算学问题的更多潜在解决方案。