Effective fake news detection using graph and summarization techniques

Nowadays, fake news is widely spreading in various media, and this fake information is causing serious damage in many areas. Therefore, there is an increasing need to accurately detect fake news to prevent such damage. In this paper, we propose a novel method that uses graph and summarization techniques for fake news detection. Our proposed method represents the relationship of all sentences in a graph structure to accurately understand the context information of the document. Accordingly, the relationship between sentences in the graph is calculated as a score through the attention mechanism. Then, the summarization technique is used to reflect the sentence subject information in the graph update process. Our proposed method shows better performance than Karimi's and BERT based models by approximately 10.34%p and 3.72%p, respectively.