Fake News Detection Using Knowledge Graph and Graph Convolutional Network

Detecting fake news problems has been a topic of research noticed in recent years. However, at present, fake news detection research has only been conducted with English data sets, no research has been conducted with Vietnamese datasets. Therefore, it is essentially important and urgent to do research on fake news detection with the Vietnamese dataset. In this research, we proposed a detecting fake news method using a knowledge graph (KG) combined with a semi-supervised learning Graph Convolutional Networks (GCN) to predict whether the news is real or fake. Our research is implemented by collecting data including real and fake news from the datasets of online newspapers in Vietnamese including vnexpress, tuoitre, etc., and VFND-vietnamese-fake-news. In addition, we will not label news that has not been verified for authenticity. We build a knowledge graph for the dataset by embedding words into the dataset using the Glove library and building a knowledge graph using the Word Mover’s Distance algorithm (WMD) combined with the K-nearest-neighbor algorithm (KNN). Detecting real or fake news models with input knowledge graph built and GCN algorithm and finally perform the tests with datasets. With our research, we have built a knowledge graph for the Vietnamese data set of real and fake news collected from 2018 to 2023 combined with the VFND-vietnamese-fake-news dataset. Our proposed method can resolve to detect fake news problems by the GCN algorithm. Research’s results have a high precision of up to 85%, in the case of labeled data accounting for 50% of the input dataset.