aspg-31-1802-1791686732 20261011024532 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group International Journal of Advances in Applied Computational Intelligence IJAACI 2833-5600 2022 2 1 Ahmed Sleem Ministry of communication and information technology, Egypt Ibrahim Elhenawy Faculty of Computers and Informatics, Zagazig University, Zagazig, Sharqiyah, 44519, Egypt With the rapid growth of social media and online news platforms, the spread of fake news has become a major problem, leading to misinformation and distrust. In this paper, we propose an attentive convolutional recurrent network (ACRN) for fake news detection, which combines convolutional learning and recurrent learning capabilities to capture both local and global temporal information. Additionally, we incorporate attention mechanisms to focus on important features and reduce noise. We evaluate our model on a publicly available dataset and compare it with state-of-the-art methods. The results show that our ACRN model outperforms the existing methods in terms of accuracy, precision, recall, and F1-score. We also perform an ablation study to demonstrate the effectiveness of our attention mechanisms. Our proposed ACRN model can applied as a reliable computation intelligence tool for detecting fake news and improving the accuracy of news verification. 2022 08 14 1802 https://creativecommons.org/licenses/by/4.0/ 10.54216/IJAACI.020101 https://www.americaspg.com/journal/31/article/1802 https://www.americaspg.com/storage/21686304617.pdf