Graph-based Fake News Detection using a Summarization Technique (2021)

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 prevent the damages caused by fake news. In this paper, we propose a novel graph-based fake news detection method using a summarization technique that uses only the document internal information. Our proposed method represents the relationship between all sentences using a graph and the reflection rate of contextual information among sentences is computed by using an attention mechanism. In addition, we improve the performance of fake news detection by utilizing summary information as an important subject of the document. The experimental results demonstrate that our method achieves high accuracy, 91.04%, that is 8.85%p better than the previous method.

## introduction recently, people are easily exposed to large amount of information in various ways with the development 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 causing a lot of social and economic damages. therefore, the need to detect fake information is increasing to prevent the damages caused by fake news, and it is being researched industrially and academically @xcite @xcite . 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 analyzing linguistic features in news, such as news content @xcite , writing styles and con-sistency @xcite , and relational structure between sentences @xcite within the news. on the other hand, that of using external information does by analyzing metadata such as aspects of news spreading @xcite and user profiles of the people spreading the news @xcite . however, a collection task for external information requires a lot of time and cost, and it is very difficult to identify external 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 detecting fake news. therefore, we propose an effective fake news detection method by structuring a context 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 generate sentence embeddings. in addition, the attention mechanism is exploited for constructing a context graph so that the different relation strength between sentences influences the contextual information of each sentence in the graph. in the context graph, nodes consist of initial sentences embeddings for all the sentences in a document and the weight of an edge is estimated by an attention score between two ended nodes of the edge. we assume that all the nodes are connected because all the sentence in a document is strong related each other. then a contextualized sentence embedding on each node is generated by reflecting the neighbor's contextual information. the contextualized sentence embedding of a node is computed by the sum of the products of the attention score between the node and its neighbor node and the initial sentence embedding of the neighbor node. because the subject of a document is very important information in identifying the content of the document and fake information is commonly related to the subject, we apply a summarization technique @xcite that can effectively capture the subject information of the document to fake news detection. by using a summarization technique, sentences containing a lot of subject information are highly ranked and the ranked scores influence the attention scores for construction of the context graph. for performance comparison with the proposed method, we implemented a baseline model that detects fake news with the sum of all sentence embeddings in a document. our proposed model shows 91.04% accuracy, that is 11.19%p better performance than baseline model. it also shows 8.85%p better performance compared to other model @xcite ) that uses the same dataset and a dependency tree structure among sentences in a document. ## notations we have a corpus d of fake and real news documents. let a document @xmath0, s 2 , ...s n and each sentence s @xmath1, word 2 , ...word l where l denotes the number of words in sentence s i . we apply the bi-lstm network to all sentences of a document and obtain the initial sentence embedding ## proposed method we propose a novel fake news detection method based on the context graph with a summarization technique (see figure 1 ). our method consists of three components. the first one is graph construction using attention mechanism for representing the relationship between all sentences using a graph. the second one is core sentence extraction for ranking sentences with subject information using a sum-marization technique. the third one is fake news detection for discriminating fake news from a lot of documents. ## analysis herein, we introduce two analyses by an additional experiment for subject consistency detection in our model and dataset. the proposed method attempted to effectively detect subject information in fake news and the consistency of subject information is also important to detect fake news. in the first analysis, we verify our process of updating the graph in the proposed model, which uses a fully connected graph, by comparing other model using a 50% connected graph, which uses edges with only the top 50% with high attention scores. as a result, the proposed model with a fully connect graph showed 1.29%p higher performance than the model with a 50% connected graph. we think it means that a fully connect graph is more useful to detect fake news. secondly, we observed the variances of the attention scores in the fake and real documents (see ## conclusions we have proposed a novel graph based fake news detection method using summarization technique. our model shows that the use of contextual and subject information is helpful in detecting fake news. our final proposed model achieved better performance than the baseline model about 11.19%p.