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

TL;DR

This paper advocates for using NLP techniques to enhance causal analysis and perception mining in social media to improve mental health inference, emphasizing explainability and clinical relevance.

Contribution

It introduces a framework focusing on causal analysis and perception mining within NLP to advance mental health analysis from social media data.

Findings

Increased research in causal relation extraction for mental health.

Advancements in perception mining for social effects on mental states.

Call for more explainable NLP models in clinical psychology.

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.