# Causal Explanation Analysis on Social Media

arXiv: https://arxiv.org/abs/1809.01202 · HTML: https://mlsanity.com/paper/1809.01202/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Sep 2018.

## Built on by (most cited first, 12 of 12)

- [CAMS: An Annotated Corpus for Causal Analysis of Mental Health Issues in Social Media Posts](https://mlsanity.com/paper/2207.04674/) (2022)
- [Deep Causal Learning: Representation, Discovery and Inference](https://mlsanity.com/paper/2211.03374/) (2022)
- [UniCausal: Unified Benchmark and Repository for Causal Text Mining](https://mlsanity.com/paper/2208.09163/) (2022)
- ["Mama Always Had a Way of Explaining Things So I Could Understand'': A Dialogue Corpus for Learning to Construct Explanations](https://mlsanity.com/paper/2209.02508/) (2022)
- [Explainable Causal Analysis of Mental Health on Social Media Data](https://mlsanity.com/paper/2210.08430/) (2022)
- [A Multi-Level Benchmark for Causal Language Understanding in Social Media Discourse](https://mlsanity.com/paper/2509.16722/) (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)
- [Modeling the Quality of Dialogical Explanations](https://mlsanity.com/paper/2403.00662/) (2024)
- [Capturing Human Cognitive Styles with Language: Towards an Experimental Evaluation Paradigm](https://mlsanity.com/paper/2502.13326/) (2025)
- [Causal Categorization of Mental Health Posts using Transformers](https://mlsanity.com/paper/2301.02589/) (2023)
- [Multi-class Categorization of Reasons behind Mental Disturbance in Long Texts](https://mlsanity.com/paper/2304.04118/) (2023)
- [To Explain Or Not To Explain: An Empirical Investigation Of AI-Based Recommendations On Social Media Platforms](https://mlsanity.com/paper/2508.16610/) (2025)

## Abstract (arXiv)

Understanding causal explanations - reasons given for happenings in one's life - has been found to be an important psychological factor linked to physical and mental health. Causal explanations are often studied through manual identification of phrases over limited samples of personal writing. Automatic identification of causal explanations in social media, while challenging in relying on contextual and sequential cues, offers a larger-scale alternative to expensive manual ratings and opens the door for new applications (e.g. studying prevailing beliefs about causes, such as climate change). Here, we explore automating causal explanation analysis, building on discourse parsing, and presenting two novel subtasks: causality detection (determining whether a causal explanation exists at all) and causal explanation identification (identifying the specific phrase that is the explanation). We achieve strong accuracies for both tasks but find different approaches best: an SVM for causality prediction (F1 = 0.791) and a hierarchy of Bidirectional LSTMs for causal explanation identification (F1 = 0.853). Finally, we explore applications of our complete pipeline (F1 = 0.868), showing demographic differences in mentions of causal explanation and that the association between a word and sentiment can change when it is used within a causal explanation.