Enhancing Fake News Detection: A Multimodal Approach Integrating Machine Learning

Abstract: Fake news on digital platforms is a significant threat to information integrity, shaping public opinion and eroding trust in media sources. This project proposes a comprehensive approach to fake news detection using a [2]multimodal framework that combines [5]Machine learning techniques for text, images, and metadata. Unlike our approach, existing systems use [10]convolutional neural Networks (CNNs) for image analysis and [12]natural language processing (NLP) models for text analysis and metadata to get a holistic view of news content.

To build a robust detection mechanism, we use a diverse dataset with real and fake news articles, manipulated images, and misleading [4]metadata.

The results show a significant improvement in detection [8]accuracy over single-modal models, with high precision and [8]recall. This project not only contributes to the field of fake news detection but also highlights the importance of ethical considerations in [3]AI systems. Future work will be to extend the model to detect deepfakes and misinformation in multimedia content and apply it to real-world scenarios.

Fake News Detection, multimodal Framework, machine learning, convolutional neural Networks (CNN), natural language processing (NLP),, Metadata analysis, Algorithmic Transparency, Bias Mitigation, Ethical AI, Ensemble Approach, Digital Misinformation, Misinformation Detection, Multimedia Analysis, Fact-Checking, Deepfake Detection

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