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.

翻译：摘要：社交媒体上的人类互动常传达行为背后的意图，为在线用户的心理健康分析提供了心理语言资源。计算智能技术从这类社交媒体资源推断精神疾病方面的成功，表明自然语言处理可成为因果分析和感知挖掘的透镜。然而，我们认为，为了对临床心理学实践和个性化心理健康护理产生最佳影响，仍需开展更具因果性和可解释性的研究。为弥合这一差距，我们提出两个重要维度：（1）因果分析，用于说明用户生成文本中的因果关系；（2）感知挖掘，用于推断社会效应如何影响在线用户意图的心理视角。在自然语言处理范畴内，我们进一步通过话语分析的最新进展，探索与这两个维度相关的关键研究领域。这篇立场文章旨在引导学界探索该领域的解决方案，并推动开发从社交媒体推断心理健康的对话代理的实践水平。我们主张通过语言透镜对计算心理学问题进行更可解释的建模——因为观察到在推断心理状态时，已有越来越多的研究致力于数据集构建与问题形式化，以促进因果关联提取与感知增强。