FakeWatch: A Framework for Detecting Fake News to Ensure Credible Elections
This work addresses the threat of fake news to election integrity, but it is incremental as it applies existing methods to a new dataset.
The paper tackles the problem of fake news detection during elections by introducing FakeWatch, a framework that uses a new dataset of North American election-related news articles and integrates traditional ML and state-of-the-art language models, finding that language models have a slight edge but traditional models remain competitive in accuracy and efficiency.
In today's technologically driven world, the rapid spread of fake news, particularly during critical events like elections, poses a growing threat to the integrity of information. To tackle this challenge head-on, we introduce FakeWatch, a comprehensive framework carefully designed to detect fake news. Leveraging a newly curated dataset of North American election-related news articles, we construct robust classification models. Our framework integrates a model hub comprising of both traditional machine learning (ML) techniques, and state-of-the-art Language Models (LMs) to discern fake news effectively. Our objective is to provide the research community with adaptable and precise classification models adept at identifying fake news for the elections agenda. Quantitative evaluations of fake news classifiers on our dataset reveal that, while state-of-the-art LMs exhibit a slight edge over traditional ML models, classical models remain competitive due to their balance of accuracy and computational efficiency. Additionally, qualitative analyses shed light on patterns within fake news articles. We provide our labeled data at https://huggingface.co/datasets/newsmediabias/fake_news_elections_labelled_data and model https://huggingface.co/newsmediabias/FakeWatch for reproducibility and further research.