In the era of social media platforms, identifying the credibility of online content is crucial to combat misinformation. We present the CREDiBERT (CREDibility assessment using Bi-directional Encoder Representations from Transformers), a source credibility assessment model fine-tuned for Reddit submissions focusing on political discourse as the main contribution. We adopt a semi-supervised training approach for CREDiBERT, leveraging Reddit's community-based structure. By encoding submission content using CREDiBERT and integrating it into a Siamese neural network, we significantly improve the binary classification of submission credibility, achieving a 9% increase in F1 score compared to existing methods. Additionally, we introduce a new version of the post-to-post network in Reddit that efficiently encodes user interactions to enhance the binary classification task by nearly 8% in F1 score. Finally, we employ CREDiBERT to evaluate the susceptibility of subreddits with respect to different topics.
@article{arxiv.2402.10938,
title = {News Source Credibility Assessment: A Reddit Case Study},
author = {Arash Amini and Yigit Ege Bayiz and Ashwin Ram and Radu Marculescu and Ufuk Topcu},
journal= {arXiv preprint arXiv:2402.10938},
year = {2024}
}