  Social media is becoming a source of news for many people due to its ease and
freedom of use. As a result, fake news has been spreading quickly and easily
regardless of its credibility, especially in the last decade. Fake news
publishers take advantage of critical situations such as the Covid-19 pandemic
and the American presidential elections to affect societies negatively. Fake
news can seriously impact society in many fields including politics, finance,
sports, etc. Many studies have been conducted to help detect fake news in
English, but research conducted on fake news detection in the Arabic language
is scarce. Our contribution is twofold: first, we have constructed a large and
diverse Arabic fake news dataset. Second, we have developed and evaluated
transformer-based classifiers to identify fake news while utilizing eight
state-of-the-art Arabic contextualized embedding models. The majority of these
models had not been previously used for Arabic fake news detection. We conduct
a thorough analysis of the state-of-the-art Arabic contextualized embedding
models as well as comparison with similar fake news detection systems.
Experimental results confirm that these state-of-the-art models are robust,
with accuracy exceeding 98%.
