FakeWatch: A Framework for Detecting Fake News to Ensure Credible
  Elections
Shaina Raza Tahniat Khan Drai Paulen-Patterson Veronica Chatrath Mizanur Rahman Oluwanifemi Bamgbose
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.