Causal Explanation Analysis on Social Media
Youngseo Son, Nipun Bayas, H. Andrew Schwartz

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…
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Taxonomy
TopicsTopic Modeling · Advanced Graph Neural Networks · Sentiment Analysis and Opinion Mining
