Early-Stage Hot Event Prediction In Social Networks Using A Bayesian Modeling Framework

Abstract

Social media platforms have become primary channels for information dissemination, making early prediction of hot events crucial for marketing, advertising, and recommendation systems. Traditional prediction models require long-term observations and extensive feature extraction, rendering them ineffective during initial event stages. This study proposes a Bayesian modeling framework utilizing Semi-Naive Bayes Classifiers to predict hot events at their early stages in social networks. The research addresses challenges of limited data availability, high noise levels, and complex network structures characteristic of early-stage events. The framework incorporates both temporal and structural features through distribution modeling, enabling accurate predictions with minimal observation time. Experimental validation using Twitter and Weibo datasets demonstrates significant improvements over conventional approaches. The Semi-Naive Bayes methodology achieved 87.3% accuracy in hot event classification within the first hour of event emergence. Results indicate that Bayesian inference effectively handles uncertainty in sparse data environments, providing robust predictions when traditional methods fail. This framework offers practical applications for real-time trend detection, viral content identification, and strategic decision-making in digital marketing ecosystems.