# Causal Categorization of Mental Health Posts using Transformers

arXiv: https://arxiv.org/abs/2301.02589 · HTML: https://mlsanity.com/paper/2301.02589/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 13 references that are in the map.
- First posted on arXiv 6 Jan 2023.

## Builds on (most cited first, 10 of 10)

- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [Longformer: The Long-Document Transformer](https://mlsanity.com/paper/2004.05150/) (2020)
- [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://mlsanity.com/paper/1906.08237/) (2019)
- [Deep Learning for Suicide and Depression Identification with Unsupervised Label Correction](https://mlsanity.com/paper/2102.09427/) (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)
- [EmotionX-IDEA: Emotion BERT -- an Affectional Model for Conversation](https://mlsanity.com/paper/1908.06264/) (2019)
- [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)

## Abstract (arXiv)

With recent developments in digitization of clinical psychology, NLP research community has revolutionized the field of mental health detection on social media. Existing research in mental health analysis revolves around the cross-sectional studies to classify users' intent on social media. For in-depth analysis, we investigate existing classifiers to solve the problem of causal categorization which suggests the inefficiency of learning based methods due to limited training samples. To handle this challenge, we use transformer models and demonstrate the efficacy of a pre-trained transfer learning on "CAMS" dataset. The experimental result improves the accuracy and depicts the importance of identifying cause-and-effect relationships in the underlying text.