News Source Credibility Assessment: A Reddit Case Study
This work addresses misinformation on social media platforms like Reddit, but it is incremental as it builds on existing transformer and neural network methods for a specific domain.
The paper tackled the problem of assessing news source credibility on social media by developing CREDiBERT, a model fine-tuned for Reddit political discourse, which improved binary classification of submission credibility by 9% in F1 score and enhanced user interaction encoding by nearly 8% in F1 score.
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.