A comparative analysis of Graph Neural Networks and commonly used machine learning algorithms on fake news detection (2203.14132v1)

Abstract: Fake news on social media is increasingly regarded as one of the most concerning issues. Low cost, simple accessibility via social platforms, and a plethora of low-budget online news sources are some of the factors that contribute to the spread of false news. Most of the existing fake news detection algorithms are solely focused on the news content only but engaged users prior posts or social activities provide a wealth of information about their views on news and have significant ability to improve fake news identification. Graph Neural Networks are a form of deep learning approach that conducts prediction on graph-described data. Social media platforms are followed graph structure in their representation, Graph Neural Network are special types of neural networks that could be usually applied to graphs, making it much easier to execute edge, node, and graph-level prediction. Therefore, in this paper, we present a comparative analysis among some commonly used machine learning algorithms and Graph Neural Networks for detecting the spread of false news on social media platforms. In this study, we take the UPFD dataset and implement several existing machine learning algorithms on text data only. Besides this, we create different GNN layers for fusing graph-structured news propagation data and the text data as the node feature in our GNN models. GNNs provide the best solutions to the dilemma of identifying false news in our research.