Multi-Model Ensemble Techniques for Misinformation Detection in Social Media with Sentiment Analysis

Abstract

In recent years, the prevalence of misinformation on social media platforms has become a pressing issue, undermining trust, manipulating public opinion, and influencing various domains such as politics, healthcare, and economics. Addressing this challenge requires efficient and scalable solutions. This research investigates the use of multi-model ensemble techniques for detecting misinformation in social media posts, integrating sentiment analysis as a key component. By combining the predictions of multiple models, we aim to improve the accuracy and reliability of misinformation detection systems. The ensemble models leverage diverse approaches, including natural language processing (NLP), machine learning, and deep learning techniques, to address the complexity of misinformation, including its nuanced sentiment and misleading framing. The effectiveness of these techniques in distinguishing truthful information from false content is evaluated, highlighting the role of sentiment analysis in enhancing detection performance. The research provides insights into how these techniques can be applied in realworld settings and proposes strategies for improving their scalability, robustness, and adaptability to evolving misinformation tactics.