  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.
