Causal Explanation Analysis on Social Media

TL;DR

This paper develops automated methods to detect and identify causal explanations in social media posts, enabling large-scale analysis of how people articulate causes and beliefs, with implications for psychological and social research.

Contribution

It introduces two novel subtasks—causality detection and causal explanation identification—and demonstrates effective models for each, advancing automated analysis of causal reasoning in social media.

Findings

High accuracy in causality detection (F1=0.791)

Effective causal explanation identification (F1=0.853)

Applications reveal demographic differences and sentiment shifts within causal explanations

Abstract

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.