Leveraging Deep Learning and Ensemble Techniques for Mobile Misinformation Detection with Emotional Context

Abstract

In the current era of rapid information exchange, misinformation on mobile platforms has become a significant concern, with far-reaching consequences for individuals and societies. Mobile misinformation detection has become a challenging task due to the vast volume of content generated and shared on various social media platforms, news outlets, and messaging applications. This research explores the application of deep learning and ensemble techniques for detecting misinformation on mobile platforms, with a focus on identifying and analyzing the emotional context of the content. Deep learning methods, such as neural networks, offer the ability to capture complex patterns in textual data, while ensemble methods combine multiple models to enhance prediction accuracy. The integration of emotional context into misinformation detection allows for a more nuanced understanding of content, helping to discern between misleading information and emotionally charged but accurate data. By leveraging both deep learning and ensemble techniques, this paper aims to propose a more effective approach to combatting mobile misinformation.