Detecting Rumor Veracity with Only Textual Information by Double-Channel Structure

Alex Gunwoo Kim, Sangwon Yoon


Abstract
Kyle (1985) proposes two types of rumors: informed rumors which are based on some private information and uninformed rumors which are not based on any information (i.e. bluffing). Also, prior studies find that when people have credible source of information, they are likely to use a more confident textual tone in their spreading of rumors. Motivated by these theoretical findings, we propose a double-channel structure to determine the ex-ante veracity of rumors on social media. Our ultimate goal is to classify each rumor into true, false, or unverifiable category. We first assign each text into either certain (informed rumor) or uncertain (uninformed rumor) category. Then, we apply lie detection algorithm to informed rumors and thread-reply agreement detection algorithm to uninformed rumors. Using the dataset of SemEval 2019 Task 7, which requires ex-ante threefold classification (true, false, or unverifiable) of social media rumors, our model yields a macro-F1 score of 0.4027, outperforming all the baseline models and the second-place winner (Gorrell et al., 2019). Furthermore, we empirically validate that the double-channel structure outperforms single-channel structures which use either lie detection or agreement detection algorithm to all posts.
Anthology ID:
2022.socialnlp-1.3
Volume:
Proceedings of the Tenth International Workshop on Natural Language Processing for Social Media
Month:
July
Year:
2022
Address:
Seattle, Washington
Editors:
Lun-Wei Ku, Cheng-Te Li, Yu-Che Tsai, Wei-Yao Wang
Venue:
SocialNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
35–44
Language:
URL:
https://aclanthology.org/2022.socialnlp-1.3
DOI:
10.18653/v1/2022.socialnlp-1.3
Bibkey:
Cite (ACL):
Alex Gunwoo Kim and Sangwon Yoon. 2022. Detecting Rumor Veracity with Only Textual Information by Double-Channel Structure. In Proceedings of the Tenth International Workshop on Natural Language Processing for Social Media, pages 35–44, Seattle, Washington. Association for Computational Linguistics.
Cite (Informal):
Detecting Rumor Veracity with Only Textual Information by Double-Channel Structure (Kim & Yoon, SocialNLP 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.socialnlp-1.3.pdf
Video:
 https://aclanthology.org/2022.socialnlp-1.3.mp4