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

arXiv: https://arxiv.org/abs/2301.11004 · HTML: https://mlsanity.com/paper/2301.11004/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 74 references that are in the map.
- First posted on arXiv 26 Jan 2023.

## Builds on (most cited first, 16 of 16)

- [Knowledge Graphs](https://mlsanity.com/paper/2003.02320/) (2020)
- [A Survey of Transformers](https://mlsanity.com/paper/2106.04554/) (2021)
- [MentalBERT: Publicly Available Pretrained Language Models for Mental Healthcare](https://mlsanity.com/paper/2110.15621/) (2021)
- [CAMS: An Annotated Corpus for Causal Analysis of Mental Health Issues in Social Media Posts](https://mlsanity.com/paper/2207.04674/) (2022)
- [On the State of Social Media Data for Mental Health Research](https://mlsanity.com/paper/2011.05233/) (2020)
- [Causal Explanations and XAI](https://mlsanity.com/paper/2201.13169/) (2022)
- [Latent Suicide Risk Detection on Microblog via Suicide-Oriented Word Embeddings and Layered Attention](https://mlsanity.com/paper/1910.12038/) (2019)
- [Empathic Conversations: A Multi-level Dataset of Contextualized Conversations](https://mlsanity.com/paper/2205.12698/) (2022)
- [Causal Inference of Script Knowledge](https://mlsanity.com/paper/2004.01174/) (2020)
- [Causal Explanation Analysis on Social Media](https://mlsanity.com/paper/1809.01202/) (2018)
- [Building and Using Personal Knowledge Graph to Improve Suicidal Ideation Detection on Social Media](https://mlsanity.com/paper/2012.09123/) (2020)
- [Discourse Relation Embeddings: Representing the Relations between Discourse Segments in Social Media](https://mlsanity.com/paper/2105.01306/) (2021)
- [A Machine Learning Analysis of COVID-19 Mental Health Data](https://mlsanity.com/paper/2112.00227/) (2021)
- [Explainable Causal Analysis of Mental Health on Social Media Data](https://mlsanity.com/paper/2210.08430/) (2022)
- [Understanding Interpersonal Conflict Types and their Impact on Perception Classification](https://mlsanity.com/paper/2208.08758/) (2022)
- [Learning a General Clause-to-Clause Relationships for Enhancing Emotion-Cause Pair Extraction](https://mlsanity.com/paper/2208.13549/) (2022)

## Built on by (most cited first, 2 of 2)

- [P-ReMIS: Pragmatic Reasoning in Mental Health and a Social Implication](https://mlsanity.com/paper/2507.23247/) (2025)
- [Holistix: A Dataset for Holistic Wellness Dimensions Analysis in Mental Health Narratives](https://mlsanity.com/paper/2507.09565/) (2025)

## Abstract (arXiv)

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.