Automatic Fake News Detection in Political Platforms - A Transformer-based Approach

Shaina Raza


Abstract
The dynamics and influence of fake news on Twitter during the 2020 US presidential election remains to be clarified. Here, we use a dataset related to 2020 U.S Election that consists of news articles and tweets on those articles. Therefore, it is extremely important to stop the spread of fake news before it reaches a mass level, which is a big challenge. We propose a novel fake news detection framework that can address this challenge. Our proposed framework exploits the information from news articles and social contexts to detect fake news. The proposed model is based on a Transformer architecture, which can learn useful representations from fake news data and predicts the probability of a news as being fake or real. Experimental results on real-world data show that our model can detect fake news with higher accuracy and much earlier, compared to the baselines.
Anthology ID:
2021.case-1.10
Volume:
Proceedings of the 4th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE 2021)
Month:
August
Year:
2021
Address:
Online
Venues:
ACL | CASE | IJCNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
68–78
Language:
URL:
https://aclanthology.org/2021.case-1.10
DOI:
10.18653/v1/2021.case-1.10
Bibkey:
Cite (ACL):
Shaina Raza. 2021. Automatic Fake News Detection in Political Platforms - A Transformer-based Approach. In Proceedings of the 4th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE 2021), pages 68–78, Online. Association for Computational Linguistics.
Cite (Informal):
Automatic Fake News Detection in Political Platforms - A Transformer-based Approach (Raza, CASE 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.case-1.10.pdf
Data
RealNews