Semantic Oppositeness Assisted Deep Contextual Modeling for Automatic Rumor Detection in Social Networks

Nisansa de Silva, Dejing Dou


Abstract
Social networks face a major challenge in the form of rumors and fake news, due to their intrinsic nature of connecting users to millions of others, and of giving any individual the power to post anything. Given the rapid, widespread dissemination of information in social networks, manually detecting suspicious news is sub-optimal. Thus, research on automatic rumor detection has become a necessity. Previous works in the domain have utilized the reply relations between posts, as well as the semantic similarity between the main post and its context, consisting of replies, in order to obtain state-of-the-art performance. In this work, we demonstrate that semantic oppositeness can improve the performance on the task of rumor detection. We show that semantic oppositeness captures elements of discord, which are not properly covered by previous efforts, which only utilize semantic similarity or reply structure. We show, with extensive experiments on recent data sets for this problem, that our proposed model achieves state-of-the-art performance. Further, we show that our model is more resistant to the variances in performance introduced by randomness.
Anthology ID:
2021.eacl-main.31
Volume:
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume
Month:
April
Year:
2021
Address:
Online
Editors:
Paola Merlo, Jorg Tiedemann, Reut Tsarfaty
Venue:
EACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
405–415
Language:
URL:
https://aclanthology.org/2021.eacl-main.31
DOI:
10.18653/v1/2021.eacl-main.31
Bibkey:
Cite (ACL):
Nisansa de Silva and Dejing Dou. 2021. Semantic Oppositeness Assisted Deep Contextual Modeling for Automatic Rumor Detection in Social Networks. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pages 405–415, Online. Association for Computational Linguistics.
Cite (Informal):
Semantic Oppositeness Assisted Deep Contextual Modeling for Automatic Rumor Detection in Social Networks (de Silva & Dou, EACL 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.eacl-main.31.pdf