Intelligent Detection of Disinformation Based on Chronological and Spatial Topologies

As communication and high-speed internet make it easy to spread fake news on social media, scholars propose methods to detect it. However, existing approaches have limitations, such as reduced effectiveness without user information and high computational costs. Our proposed method, based on temporal and communication networks, is mainly used in the context of lack of user-related data and large textual datasets such as social media, forums, and online news. In sparse data settings, our proposed method can capture the propagation features of fake news for fake news detection, which is a feature extraction method based on building a propagation network for fake news detection. By studying the propagation pattern of fake news on social media, we obtain features belonging to the propagation network and test the source tweets using various machine learning classifiers. We also conduct experiments on realistic datasets to validate the method's feasibility in social network scenarios.