Authors:
Sakshi Kalra
1
;
Yashvardhan Sharma
1
;
Mehul Agrawal
1
;
Sai Mantri
1
and
Gajendra Chauhan
2
Affiliations:
1
Department of CSIS, BITS Pilani, Pilani, 333031, Rajasthan, India
;
2
Department of HSS, BITS Pilani, Pilani, 333031, Rajasthan, India
Keyword(s):
Early Fake News Detection, Neural Networks, Transformers, Attention Mechanism, User Clustering, Fuzzy C-Means Clustering, K-Means Clustering.
Abstract:
People are now consuming news on social media platforms rather than through traditional sources as a result of easy access to the internet. This has allowed for the recent rise in the online dissemination of false information. The spread of false information seriously damages people’s reputations and the public’s trust in them. The research community has recently given fake news identification a great deal of attention, and prior studies have mainly concentrated on finding hints in news content or diffusion graphs. The older models, on the other hand, didn’t have the key features needed to spot fake news quickly. We focus on finding fake news by using features that are available when it is just starting to spread. The current work suggests a new framework made up of content-based features taken from news articles and social-context features taken from user characteristics and responses at the sentence level. In addition, we extend our approach to Transformer-based models and leverage
user clustering to demonstrate a considerable performance gain over the original model.
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