Tackling Fake News Detection by Interactively Learning Representations using Graph Neural Networks

Nikhil Mehta, Dan Goldwasser


Abstract
Easy access, variety of content, and fast widespread interactions are some of the reasons that have made social media increasingly popular in today’s society. However, this has also enabled the widespread propagation of fake news, text that is published with an intent to spread misinformation and sway beliefs. Detecting fake news is important to prevent misinformation and maintain a healthy society. While prior works have tackled this problem by building supervised learning systems, automatedly modeling the social media landscape that enables the spread of fake news is challenging. On the contrary, having humans fact check all news is not scalable. Thus, in this paper, we propose to approach this problem interactively, where human insight can be continually combined with an automated system, enabling better social media representation quality. Our experiments show performance improvements in this setting.
Anthology ID:
2021.internlp-1.7
Volume:
Proceedings of the First Workshop on Interactive Learning for Natural Language Processing
Month:
August
Year:
2021
Address:
Online
Venue:
InterNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
46–53
Language:
URL:
https://aclanthology.org/2021.internlp-1.7
DOI:
10.18653/v1/2021.internlp-1.7
Bibkey:
Cite (ACL):
Nikhil Mehta and Dan Goldwasser. 2021. Tackling Fake News Detection by Interactively Learning Representations using Graph Neural Networks. In Proceedings of the First Workshop on Interactive Learning for Natural Language Processing, pages 46–53, Online. Association for Computational Linguistics.
Cite (Informal):
Tackling Fake News Detection by Interactively Learning Representations using Graph Neural Networks (Mehta & Goldwasser, InterNLP 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.internlp-1.7.pdf