  The proliferation of fake news and its propagation on social media has become
a major concern due to its ability to create devastating impacts. Different
machine learning approaches have been suggested to detect fake news. However,
most of those focused on a specific type of news (such as political) which
leads us to the question of dataset-bias of the models used. In this research,
we conducted a benchmark study to assess the performance of different
applicable machine learning approaches on three different datasets where we
accumulated the largest and most diversified one. We explored a number of
advanced pre-trained language models for fake news detection along with the
traditional and deep learning ones and compared their performances from
different aspects for the first time to the best of our knowledge. We find that
BERT and similar pre-trained models perform the best for fake news detection,
especially with very small dataset. Hence, these models are significantly
better option for languages with limited electronic contents, i.e., training
data. We also carried out several analysis based on the models' performance,
article's topic, article's length, and discussed different lessons learned from
them. We believe that this benchmark study will help the research community to
explore further and news sites/blogs to select the most appropriate fake news
detection method.
