Causal Explanation Analysis on Social Media

Youngseo Son, Nipun Bayas, H. Andrew Schwartz


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.
Anthology ID:
D18-1372
Volume:
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
Month:
October-November
Year:
2018
Address:
Brussels, Belgium
Editors:
Ellen Riloff, David Chiang, Julia Hockenmaier, Jun’ichi Tsujii
Venue:
EMNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
3350–3359
Language:
URL:
https://aclanthology.org/D18-1372
DOI:
10.18653/v1/D18-1372
Bibkey:
Cite (ACL):
Youngseo Son, Nipun Bayas, and H. Andrew Schwartz. 2018. Causal Explanation Analysis on Social Media. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 3350–3359, Brussels, Belgium. Association for Computational Linguistics.
Cite (Informal):
Causal Explanation Analysis on Social Media (Son et al., EMNLP 2018)
Copy Citation:
PDF:
https://aclanthology.org/D18-1372.pdf
Attachment:
 D18-1372.Attachment.zip