Semantic graph based topic modelling framework for multilingual fake news detection

Abstract

Fake news detection is one of the most alluring problems that has grabbed the interest of Machine Learning (ML) and Natural Language Processing (NLP) experts in recent years. The majority of existing studies on detecting fake news are written in English, restricting its application outside the English-speaking population. The lack of annotated corpora and technologies makes it difficult to identify false news in the scenario of low-resource languages, despite the growth in multilingual web content. Moreover, existing works cannot collect more semantic and contextual characteristics from documents in a particular multilingual text corpus. To bridge up these challenges and deal with the multilingual fake news detection challenge, we develop a new semantic graph attention-based representation learning framework to extract structural and semantic representations of texts. Our experiments on TALLIP fake news datasets showed that the classification performance had been significantly enhanced, ranging from 1% to 7% in terms of accuracy metric, and our proposed framework outperformed the state-of-the-art techniques for the multilingual fake news detection task.

Keywords

• Fake news detection
• Cross-language study
• Graph attention network