Impact of Transformer-Based Models and User Clustering in Early Fake News Detection in Social Media

Sakshi Kalra, Yashvardhan Sharma, Mehul Agrawal, Sai Mantri, Gajendra Chauhan

2023

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.

Paper Citation

in Harvard Style

Kalra S., Sharma Y., Agrawal M., Mantri S. and Chauhan G. (2023). Impact of Transformer-Based Models and User Clustering in Early Fake News Detection in Social Media. In Proceedings of the 12th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-626-2, pages 889-896. DOI: 10.5220/0011684000003411

in Bibtex Style

@conference{icpram23,
author={Sakshi Kalra and Yashvardhan Sharma and Mehul Agrawal and Sai Mantri and Gajendra Chauhan},
title={Impact of Transformer-Based Models and User Clustering in Early Fake News Detection in Social Media},
booktitle={Proceedings of the 12th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2023},
pages={889-896},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011684000003411},
isbn={978-989-758-626-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 12th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Impact of Transformer-Based Models and User Clustering in Early Fake News Detection in Social Media
SN - 978-989-758-626-2
AU - Kalra S.
AU - Sharma Y.
AU - Agrawal M.
AU - Mantri S.
AU - Chauhan G.
PY - 2023
SP - 889
EP - 896
DO - 10.5220/0011684000003411