FakeWatch: A Framework for Detecting Fake News to Ensure Credible Elections

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 cutting-edge Language Models (LMs) to discern fake news effectively. Our overarching objective is to provide the research community with adaptable and precise classification models adept at identifying the ever-evolving landscape of misinformation. 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. This research lays the groundwork for future endeavors aimed at combating misinformation, particularly concerning electoral processes. We provide our labeled data and model publicly for use and reproducibility.

FakeWatch ElectionShield: A Benchmarking Framework to Detect Fake News for Credible US Elections

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.