Big Data Analysis for Detecting Fake News and Rumors on social media Using Machine Learning: review
review
DOI:
https://doi.org/10.64059/eiu.v1i1.64Keywords:
Fake News, Big Data, Machine Learning, Misinformation DetectionAbstract
The proliferation of social media has transformed the landscape of information dissemination. While it has enabled rapid communication and outreach, it has also provided fertile ground for the propagation of misinformation and fake news. This paper investigates the integration of big data analytics and machine learning (ML) techniques to enhance the detection and mitigation of fake news and rumors across various social media platforms. This paper explores recent advancements in traditional ML algorithms, deep learning (DL) models, transformer-based architectures, and hybrid methods that combine multiple analytical approaches. Key challenges are addressed, including data imbalance, model bias, the increasing sophistication of AI-generated content, and the need for explainable AI. Furthermore, we emphasize the importance of multimodal analysis, incorporating textual, visual, and user-based features, and discuss the necessity for continuous model adaptation in response to evolving misinformation tactics. This study contributes to the growing body of research by presenting a synthesized framework for future research and system development in fake news detection.
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