FakeWatch ElectionShield: A Benchmarking Framework to Detect Fake News for Credible US Elections
This work addresses misinformation in elections for the research community, but it is incremental as it builds on existing methods with a new dataset and benchmarking.
The researchers tackled fake news detection for US elections by introducing the FakeWatch ElectionShield framework, which includes a novel dataset and model hub, finding that state-of-the-art language models slightly outperform traditional models but classical models remain competitive in accuracy, explainability, and efficiency.
In today's technologically driven world, the spread of fake news, particularly during crucial events such as elections, presents an increasing challenge to the integrity of information. To address this challenge, we introduce FakeWatch ElectionShield, an innovative framework carefully designed to detect fake news. We have created a novel dataset of North American election-related news articles through a blend of advanced language models (LMs) and thorough human verification, for precision and relevance. We propose a model hub of LMs for identifying fake news. Our goal is to provide the research community with adaptable and accurate classification models in recognizing the dynamic nature of misinformation. Extensive evaluation of fake news classifiers on our dataset and a benchmark dataset shows our that while state-of-the-art LMs slightly outperform the traditional ML models, classical models are still competitive with their balance of accuracy, explainability, and computational efficiency. This research sets the foundation for future studies to address misinformation related to elections.