Transactions on Machine Intelligence 2821-1693 3 3 2020 09 01 Model for Detecting Fake News on Twitter 149 158 208994 10.47176/TMI.2020.149 EN M. Narangi Fard Department of Network Science and Technology, School of Advanced Science and Technology, University of Tehran, Iran Z. Heshmati Assistant Professor, Department of Network Science and Technology, School of Advanced Science and Technology, University of Tehran, Iran Journal Article 2020 05 04 Due to the widespread use of social networks by people of all ages, distinguishing between fake and real news on these platforms has become a significant challenge in today's world. Individuals who disseminate fake news on social networks often aim to achieve various commercial, political, and economic goals. Therefore, identifying and distinguishing real news from fake news is crucial in addressing this issue. The objective of this research is to present an intelligent model for detecting fake news using a news propagation tree in the social network Twitter. The dataset used in this study is sourced from the political news section of the Fake News Net website. Initially, a news propagation tree was constructed for both real and fake news using this dataset, followed by the development of features from structural, temporal, and syntactic perspectives based on the news propagation tree. Finally, machine learning algorithms were employed to build a model for predicting fake and real news. The results indicated that among the algorithms used for modeling, the Random Forest algorithm, with an accuracy of 75.8%, was the best model for distinguishing fake news from real news. https://www.tmachineintelligence.ir/article_208994_404dd771bf265cbd4142d1ec60ba177a.pdf