Deep Learning-based Sentiment Analysis for Detecting and Addressing Social Media Rumour Spreading

Abstract

This research introduces a novel deep learning-based framework for the detection and mitigation of social media rumor spreading through a combination of sentiment analysis and rumor detection. Leveraging advanced neural network architectures, including recurrent neural networks and additional features such as user engagement and source credibility, the model aims to provide a comprehensive understanding of the contextual factors influencing the dissemination of rumors. The numerical example demonstrates the model's capacity to capture nuanced sentiment dynamics in social media posts, enabling the identification of potential rumors by considering both emotional tone and network-related features. The proposed approach holds promise for enhancing the accuracy of rumor detection systems, fostering more informed content moderation strategies, and contributing to the broader efforts aimed at combating misinformation in online communities. Ethical considerations, interpretability, and future directions for model refinement are integral aspects addressed in the research, emphasizing a holistic approach to addressing the complex challenges posed by social media rumor spreading