In depth analysis for securing the truth: Addressing the fake news challenge with graph neural networks

The fake news phenomenon has a significant impact on societies, homeland security, democracy and the functioning of the public space. The spread of false information is becoming an increasing challenge in the context of the dynamic growth of the volume of content shared by news outlets and social media. The overwhelming amount of this information makes manual verification of every news item or press release practically impossible. The current development of technology in the field of natural language processing (NLP) opens up new possibilities for the development of automatic content verification systems. The automation of this process not only improves but also significantly speeds up the detection of unreliable information, which is a key tool in the fight against fake news. In this article, we propose an innovative approach that involves a multi-factor assessment of the content of documents, as opposed to the frequently used approach of binary classification into fake and non-fake. Our classification system is based on analysis using graph neural networks, which allows for a more complex and contextual understanding of the data. The obtained results indicate a significant improvement in effectiveness compared to the baseline approaches, which suggests a potential for enhanced mitigation of misinformation dissemination. • innovative approach with a multi-factor assessment of the content of documents. • NLP analysis to detect fake news. • innovative graph neural networks, which allows for a more complex and contextual understanding of the data. • results indicate a significant improvement in effectiveness compared to the baseline approaches.