Abstract
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.
Abstract (translated)
社交媒体上的人类互动往往传达其行动背后的意图,生成的心理卫生分析(MHA)心理语言资源。计算智能技术(CIT)从这些社交媒体资源中推断精神健康问题的成功表明,自然语言处理(NLP)可以作为因果关系分析和感知挖掘的透镜。然而,我们认为,对于对临床心理学实践和个性化心理卫生的最佳影响,需要更多的有重要性且可解释的研究。为了填补这一差距,我们提出了两个重要的维度:(1)因果关系分析,以在用户生成文本中展示因果关系;(2)感知挖掘,以推断社交媒体对在线用户意图的心理效应。在自然语言处理(NLP)的范围内,我们进一步探索与这两个维度相关的 critical areas of inquiry,特别通过言语分析最近的进展。这篇论文指导社区在这个领域中探索解决方案,并推动开发从社交媒体推断心理卫生的实践。我们倡导一种更加可解释的方法,通过语言的视角建模计算心理学问题,因为我们观察到在数据集和问题制定中增加的研究贡献,以提取因果关系和增强感知,同时推断心理状态。
URL
https://arxiv.org/abs/2301.11004
