Fake News Detection Landscape: Datasets, Data Modalities, AI Approaches, Their Challenges, and Future Perspectives

Abstract

Social media platforms have transformed the world into a global village by providing a unique platform for unrestricted communication and opinion sharing. However, this freedom is used to spread misinformation and disrupt societal harmony. To combat misinformation and fake news on social media platforms, multifarious AI applications have been developed to detect such content in various languages and data modalities, including text, images, and videos. To establish a distinctive platform that fosters the rapid development of AI-driven fake news detectors, researchers have published several review articles in recent years. However, many of these articles are outdated, and lack comprehensiveness, particularly regarding recent trends, public datasets, representation learning methods, and classifiers details. The limited scope of articles hindered their ability to provide in-depth information on predictors’ language specificity, data modality focus, and state-of-the-art performance across diverse languages. This paper offers a unique platform with detailed information on these aspects to address this gap. It offers a detailed roadmap for understanding the scope, strengths, and limitations of existing review articles on the subject. To support the development of new benchmark datasets and more accurate predictors, it conducts an in-depth analysis of the definitions and perspectives of fake news within the context of existing literature. This analysis provides a more comprehensive definition and basic concepts of fake news. In addition, 310 fake news detection articles published in the last 8 years have been thoroughly investigated. Within the landscape of these articles, the study presents details of languages, datasets, data modalities, predictor architecture designs, and their performance metrics.