Early Prediction of Hot Events in Social Networks with Bayesian Models

This research evaluates user activity data and uses Bayesian networks to anticipate social media trending events. The method uses Bayesian inference to identify early-stage hot events by looking at post shares, comments, and likes. The model's predicted accuracy is increased by key elements taken from engagement measurements, which also reveal potentially viral subjects. By assisting companies and marketers in identifying trends early on, this method makes tailored content strategies possible. Because of its medium level of complexity, the project is an excellent instrument for social media analytics that does not demand special equipment, making it an invaluable resource for trend analysis and digital marketing.