Hybrid Ensemble Approaches for Accurate Misinformation Detection in Textual Data

Abstract

The rise of misinformation on digital platforms presents significant challenges to both individuals and societies. The increasing volume of text-based information, combined with its complex nature, requires advanced methods for effective detection and mitigation of misleading content. Traditional single-model approaches to misinformation detection often struggle with nuances and varying formats of fake news. In this paper, we explore the potential of hybrid ensemble models to address these challenges, combining the strengths of multiple individual algorithms to improve the accuracy and robustness of misinformation detection in text. Our study focuses on combining classical machine learning techniques with deep learning models, leveraging ensemble methods such as bagging, boosting, and stacking to enhance the overall performance. By integrating different models, we aim to achieve a more comprehensive understanding of misinformation, improving generalization across diverse datasets and media types. Through various experimental setups, we demonstrate the advantages of these hybrid models in comparison to traditional approaches, presenting a promising avenue for future research in combating misinformation.