A SYSTEMATIC REVIEW ON FAKE NEWS DETECTION USING MACHINE LEARNING APPROACHES
Sourabh · Zenodo (CERN European Organization for Nuclear Research) · 2023
A Review of Methods and Approaches" is a comprehensive review paper that explores the various methods and approaches employed in the detection of fake news. The paper provides an extensive overview of the existing literature, summarizing the key techniques and algorithms utilized in this field.The review highlights the importance of addressing the growing problem of fake news, particularly in the context of evolving communication channels and social media platforms. It emphasizes the need for effective detection mechanisms to combat the spread of misinformation and disinformation.The paper covers a wide range of approaches, including machine learning, natural language processing (NLP), deep learning, network analysis, and information retrieval. It delves into the advantages and limitations of each method, providing insights into their applicability and performance.Furthermore, the review addresses the challenges faced in this domain, such as limited datasets, lack of ground truth labels, and the dynamic nature of fake news. It also discusses the importance of feature engineering, dataset construction, and evaluation metrics for accurate and reliable detection.One notable aspect of the review is its focus on comparative evaluations of different approaches. It presents studies that benchmark various methods against each other, enabling readers to understand their relative strengths and weaknesses.