CHoRaL: Collecting Humor Reaction Labels from Millions of Social Media Users

Zixiaofan Yang, Shayan Hooshmand, Julia Hirschberg


Abstract
Humor detection has gained attention in recent years due to the desire to understand user-generated content with figurative language. However, substantial individual and cultural differences in humor perception make it very difficult to collect a large-scale humor dataset with reliable humor labels. We propose CHoRaL, a framework to generate perceived humor labels on Facebook posts, using the naturally available user reactions to these posts with no manual annotation needed. CHoRaL provides both binary labels and continuous scores of humor and non-humor. We present the largest dataset to date with labeled humor on 785K posts related to COVID-19. Additionally, we analyze the expression of COVID-related humor in social media by extracting lexico-semantic and affective features from the posts, and build humor detection models with performance similar to humans. CHoRaL enables the development of large-scale humor detection models on any topic and opens a new path to the study of humor on social media.
Anthology ID:
2021.emnlp-main.364
Volume:
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
Month:
November
Year:
2021
Address:
Online and Punta Cana, Dominican Republic
Editors:
Marie-Francine Moens, Xuanjing Huang, Lucia Specia, Scott Wen-tau Yih
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
4429–4435
Language:
URL:
https://aclanthology.org/2021.emnlp-main.364
DOI:
10.18653/v1/2021.emnlp-main.364
Bibkey:
Cite (ACL):
Zixiaofan Yang, Shayan Hooshmand, and Julia Hirschberg. 2021. CHoRaL: Collecting Humor Reaction Labels from Millions of Social Media Users. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 4429–4435, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.
Cite (Informal):
CHoRaL: Collecting Humor Reaction Labels from Millions of Social Media Users (Yang et al., EMNLP 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.emnlp-main.364.pdf
Video:
 https://aclanthology.org/2021.emnlp-main.364.mp4