From Data Mining to Predictive Analytics: Progress in Understanding and Forecasting Social Media Virality

This paper explores the evolution of data mining techniques into more sophisticated forms of predictive analytics, with a particular focus on their application in understanding and forecasting the virality of social media content within the framework of Society 5.0. The study begins by addressing the role of large-scale data analysis in identifying significant behavioral patterns, correlations, and insights that can serve as early indicators of social media performance. As online interactions have become increasingly dynamic and influenced by numerous social and psychological variables, the research approach has gradually shifted from simple descriptive analytics, which primarily explain past behaviors, toward advanced predictive modeling aimed at forecasting future engagement trends. The methodology integrates a systematic review of social parameters that affect content virality, including factors such as emotional appeal, timing, and user network structures. These social indicators are then examined using data analytics tools capable of enhancing the predictive accuracy of a proposed virality model. By embedding predictive mechanisms into the analytical process, the study enables real-time decision-making in the design and dissemination of digital content. The findings have significant practical implications, particularly for digital marketers, social media strategists, and content creators seeking to optimize their reach and engagement. Ultimately, this research not only presents conceptual framework and methodological progression but also highlights the broader impact of predictive analytics in advancing strategies for digital marketing and enriching the academic discourse on social media research.