Fake news detection for Arabic headlines-articles news data using deep learning - Hassan Najadat; Mais Tawalbeh; Rasha Awawdeh (International Journal of Electrical and Computer Engineering (IJECE), 2022)
Fake news has become increasingly prevalent in recent years. The evolution of social websites has spurred the expansion of fake news causing it to a mixture with truthful information. English fake news detection had the largest share of studies, unlike Arabic fake news detection, which is still very limited. Fake news phenomenon has changed people and social perspectives through revolts in several Arab countries. False news results in the distortion of reality ignite chaos and stir public judgments. This paper provides an Arabic fake news detection approach using different deep learning models including long short-term memory and convolutional neural network based on article-headline pairs to differentiate if a news headline is in fact related or unrelated to the parallel news article. In this paper, a dataset created about the war in Syria and related to the Middle East political issues is utilized. The whole data comprises 422 claims and 3,042 articles. The models yield promising results.