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.
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Narangi Fard,M. and Heshmati,Z. (2020). Model for Detecting Fake News on Twitter. Transactions on Machine Intelligence, 3(3), 149-158. doi: 10.47176/TMI.2020.149
MLA
Narangi Fard,M. , and Heshmati,Z. . "Model for Detecting Fake News on Twitter", Transactions on Machine Intelligence, 3, 3, 2020, 149-158. doi: 10.47176/TMI.2020.149
HARVARD
Narangi Fard M., Heshmati Z. (2020). 'Model for Detecting Fake News on Twitter', Transactions on Machine Intelligence, 3(3), pp. 149-158. doi: 10.47176/TMI.2020.149
CHICAGO
M. Narangi Fard and Z. Heshmati, "Model for Detecting Fake News on Twitter," Transactions on Machine Intelligence, 3 3 (2020): 149-158, doi: 10.47176/TMI.2020.149
VANCOUVER
Narangi Fard M., Heshmati Z. Model for Detecting Fake News on Twitter. Trans. Mach. Intell., 2020; 3(3): 149-158. doi: 10.47176/TMI.2020.149
