Graph-based Fake News Detection using a Summarization Technique

Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics, pages 3276–3280 April 19 - 23, 2021. ©2021 Association for Computational Linguistics 3276 Graph-based Fake News Detection using a Summarization Technique Gihwan Kim and Youngjoong Ko Department of Computer Science and Engineering Sungkyunkwan University Suwon, Gyeonggi-do, Korea kimgihwan3364@gmail.com, yjko@skku.edu Abstract Nowadays, fake news is spreading in various ways, and this fake information is causing a lot of social damages. Thus the need to detect fake information is increasing to pre- vent the damages caused by fake news. In this paper, we propose a novel graph-based fake news detection method using a summa- rization technique that uses only the document internal information. Our proposed method represents the relationship between all sen- tences using a graph and the reflection rate of contextual information among sentences is computed by using an attention mecha- nism. In addition, we improve the perfor- mance of fake news detection by utilizing sum- mary information as an important subject of the document.The experimental results demon- strate that our method achieves high accuracy, 91.04%, that is 8.85%p better than the previ- ous method. 1 Introduction Recently, people are easily exposed to large amount of information in various ways with the develop- ment of information propagation methods. But some of the large amount of information contains fake information generated for malicious purposes. This fake information is confusing people and caus- ing a lot of social and economic damages. There- fore, the need to detect fake information is increas- ing to prevent the damages caused by fake news, and it is being researched industrially and academi- cally (Yang et al., 2012; Castillo et al., 2011; Yan et al., 2015). Prior fake news detection studies have methods of detecting fake news using external information as well as internal information. The method of using internal information detects fake news by an- alyzing linguistic features in news, such as news content (Levi et al., 2019), writing styles and con- sistency (Potthast et al., 2018), and relational struc- ture between sentences (Karimi and Tang, 2019) within the news. On the other hand, that of using external information does by analyzing metadata such as aspects of news spreading (Monti et al., 2019) and user profiles of the people spreading the news (Lu and Li, 2020). However, a collection task for external information requires a lot of time and cost, and it is very difficult to identify exter- nal information in all documents. In addition, it is more basic and important to understand a document with internal information, and the construction of structural relationships among sentences within a document is one of effective methods for detect- ing fake news. Therefore, we propose an effective fake news detection method by structuring a con- text graph for representing the relationships among sentences based on summarization information. Since all sentences in a document are strongly related to each other, the contextual information with other sentences should be reflected to gener- ate sentence embeddings. In addition, …

Content truncated to 500 words