Automated Early Detection of Misinformation on Social Media: A Large Language Model Approach with High-Volume Facebook Data

Social media platforms have become a primary conduit for the rapid dissemination of information, where the unchecked spread of misinformation poses a significant threat to public discourse and societal well-being. Introducing an innovative approach that leverages the advanced capabilities of a fine-tuned ChatGPT model, this research addresses the urgent need for scalable and accurate methods to detect misinformation in real-time across vast digital landscapes. The model was meticulously evaluated through a series of experiments that demonstrated its superior performance in identifying misleading content, particularly when compared to traditional machine learning classifiers and earlier versions of language models. The integration of comprehensive preprocessing techniques, alongside refined confidence thresholds and post-processing rules, enhanced the model's ability to process complex and diverse datasets, resulting in highly reliable predictions. The findings underscore the potential of this approach to significantly mitigate the spread of misinformation, offering a scalable solution capable of operating effectively in the fast-paced environment of social media. By advancing the field of misinformation detection, this study provides critical insights and tools that can be applied both in research and in the practical domain of content moderation, contributing to a more informed and resilient digital society.