Recognizing Counterfactual Thinking in Social Media Texts

Youngseo Son, Anneke Buffone, Joe Raso, Allegra Larche, Anthony Janocko, Kevin Zembroski, H Andrew Schwartz, Lyle Ungar


Abstract
Counterfactual statements, describing events that did not occur and their consequents, have been studied in areas including problem-solving, affect management, and behavior regulation. People with more counterfactual thinking tend to perceive life events as more personally meaningful. Nevertheless, counterfactuals have not been studied in computational linguistics. We create a counterfactual tweet dataset and explore approaches for detecting counterfactuals using rule-based and supervised statistical approaches. A combined rule-based and statistical approach yielded the best results (F1 = 0.77) outperforming either approach used alone.
Anthology ID:
P17-2103
Original:
P17-2103v1
Version 2:
P17-2103v2
Volume:
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
Month:
July
Year:
2017
Address:
Vancouver, Canada
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
654–658
Language:
URL:
https://www.aclweb.org/anthology/P17-2103
DOI:
10.18653/v1/P17-2103
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/P17-2103.pdf
Note:
 P17-2103.Notes.pdf