Sentiment-Aware Hybrid Ensembles for Enhanced Misinformation Detection in Text

Abstract

The rapid spread of misinformation has become a significant concern, particularly in the digital age, where social media platforms amplify false narratives. Detecting misinformation in the vast sea of online content requires advanced techniques capable of analyzing both the factual accuracy and the emotional tone of the text. This paper explores the role of sentiment-aware ensemble models in detecting misinformation. By combining multiple machine learning models that account for both sentiment and factual content, these models offer a more robust approach to identifying false information. We explore the theoretical foundations, practical implications, and potential advancements in integrating sentiment analysis with misinformation detection, providing insights into improving the accuracy and scalability of detection systems.