# Explainable Causal Analysis of Mental Health on Social Media Data

arXiv: https://arxiv.org/abs/2210.08430 · HTML: https://mlsanity.com/paper/2210.08430/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 15 references that are in the map.
- First posted on arXiv 16 Oct 2022.

## Builds on (most cited first, 9 of 9)

- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Axiomatic Attribution for Deep Networks](https://mlsanity.com/paper/1703.01365/) (2017)
- [A Survey of the State of Explainable AI for Natural Language Processing](https://mlsanity.com/paper/2010.00711/) (2020)
- [Post-hoc Interpretability for Neural NLP: A Survey](https://mlsanity.com/paper/2108.04840/) (2021)
- [CAMS: An Annotated Corpus for Causal Analysis of Mental Health Issues in Social Media Posts](https://mlsanity.com/paper/2207.04674/) (2022)
- [A Fine-grained Interpretability Evaluation Benchmark for Neural NLP](https://mlsanity.com/paper/2205.11097/) (2022)
- [Causal Explanation Analysis on Social Media](https://mlsanity.com/paper/1809.01202/) (2018)
- [Discourse Relation Embeddings: Representing the Relations between Discourse Segments in Social Media](https://mlsanity.com/paper/2105.01306/) (2021)
- [Towards Causal Explanation Detection with Pyramid Salient-Aware Network](https://mlsanity.com/paper/2009.10288/) (2020)

## Built on by (most cited first, 6 of 6)

- [Explainable AI for Mental Disorder Detection via Social Media: A survey and outlook](https://mlsanity.com/paper/2406.05984/) (2024)
- [Explainable AI for Clinical Outcome Prediction: A Survey of Clinician Perceptions and Preferences](https://mlsanity.com/paper/2502.20478/) (2025)
- [NLP as a Lens for Causal Analysis and Perception Mining to Infer Mental Health on Social Media](https://mlsanity.com/paper/2301.11004/) (2023)
- [Conceptualizing Suicidal Behavior: Utilizing Explanations of Predicted Outcomes to Analyze Longitudinal Social Media Data](https://mlsanity.com/paper/2312.08299/) (2023)
- [Multi-class Categorization of Reasons behind Mental Disturbance in Long Texts](https://mlsanity.com/paper/2304.04118/) (2023)
- [Holistix: A Dataset for Holistic Wellness Dimensions Analysis in Mental Health Narratives](https://mlsanity.com/paper/2507.09565/) (2025)

## Abstract (arXiv)

With recent developments in Social Computing, Natural Language Processing and Clinical Psychology, the social NLP research community addresses the challenge of automation in mental illness on social media. A recent extension to the problem of multi-class classification of mental health issues is to identify the cause behind the user's intention. However, multi-class causal categorization for mental health issues on social media has a major challenge of wrong prediction due to the overlapping problem of causal explanations. There are two possible mitigation techniques to solve this problem: (i) Inconsistency among causal explanations/ inappropriate human-annotated inferences in the dataset, (ii) in-depth analysis of arguments and stances in self-reported text using discourse analysis. In this research work, we hypothesise that if there exists the inconsistency among F1 scores of different classes, there must be inconsistency among corresponding causal explanations as well. In this task, we fine tune the classifiers and find explanations for multi-class causal categorization of mental illness on social media with LIME and Integrated Gradient (IG) methods. We test our methods with CAMS dataset and validate with annotated interpretations. A key contribution of this research work is to find the reason behind inconsistency in accuracy of multi-class causal categorization. The effectiveness of our methods is evident with the results obtained having category-wise average scores of 81.29 % and 0.906 using cosine similarity and word mover's distance, respectively.