A Review of Social Network Popularity Prediction Based on Deep Learning

Abstract

This paper explores the role of deep learning in predicting the popularity of social network content, with a focus on improving prediction accuracy through the integration of various data types, such as text, images, and user behavior. Traditional methods of predicting content popularity often fall short when handling large-scale, unstructured data, making deep learning a crucial advancement. The paper discusses the benefits of multi-modal data integration, where combining different types of data leads to more robust models capable of capturing the complexities of user engagement and content virality. Additionally, the integration of reinforcement learning and real-time prediction systems is highlighted as an emerging direction, enabling models to adapt and improve based on real-time feedback. As deep learning continues to evolve, it offers significant potential for more accurate, scalable, and adaptive models that can respond to changing trends and enhance content dissemination strategies across social media platforms.