arXiv 2301.110042023-08-23cs.CL

NLP作为因果分析与感知挖掘的透镜以推断社交媒体上的心理健康状况

NLP as a Lens for Causal Analysis and Perception Mining to Infer Mental Health on Social Media

Mayo Clinic（梅奥诊所）；The Chinese University of Hong Kong（香港中文大学）；The University of Sydney（悉尼大学）；University of Florida（佛罗里达大学）

Muskan Garg, Chandni Saxena, Usman Naseem, Bonnie J Dorr

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AI总结：

本文提出将NLP作为因果分析与感知挖掘的透镜用于社交媒体心理健康推断，明确两大研究维度，探索话语分析等相关NLP领域，倡导可解释的计算心理学研究，以推动相关对话智能体发展。

摘要：

人类在社交媒体上的互动往往传达出其行为背后的意图，为在线用户的心理健康分析（Mental Health Analysis, MHA）提供了一种心理学语言资源。计算智能技术（Computational Intelligence Techniques, CIT）在从此类社交媒体资源推断精神疾病方面取得的成功，表明自然语言处理（NLP）可作为因果分析与感知挖掘的透镜。然而我们认为，需要开展更具影响力和可解释性的研究，才能对临床心理学实践和个性化心理健康护理产生最优效果。为弥补这一差距，我们提出两个重要维度：（1）因果分析，用以阐明用户生成文本中的因果关系；（2）感知挖掘，用以推断社会效应对在线用户意图的心理学视角。在自然语言处理（NLP）范畴内，我们进一步探索了与这两个维度相关的关键研究领域，特别是借助话语分析的最新进展。本立场论文旨在引导学界探索该领域的解决方案，推动开发从社交媒体推断心理健康状况的对话智能体的实践水平。我们倡导通过语言透镜，以更具可解释性的方法建模计算心理学问题，因为我们观察到，在推断心理状态时，因果关系提取和感知增强相关的数据集与问题构建方面的研究贡献正不断增多。

英文摘要：

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.

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