Real-Time Event Detection in Social Media Streams using Stream Data Mining

Abstract

Abstract: The need for real-time event detection has grown rapidly in the modern world of instantaneous information transmission through social media. In order to implement real-time event detection inside the dynamic environment of social media streams, this research article provides a ground-breaking framework that harnesses the power of stream data mining techniques. The combination of three different stream data mining algorithms—Sliding Window Analysis, Burst Detection using K-Means, and Agglomerative Hierarchical Clustering—allows us to tackle this problem. Together, these algorithms make it possible to extract important patterns, shedding light on how events arise inside social media streams. Utilizing cutting-edge stream data mining techniques, this study introduces a novel framework for real-time event detection within social media streams. The quick identification and monitoring of real-world events take on critical importance in the modern environment of rapid information dissemination through social media channels. The dynamic and high-velocity characteristics of social media data streams present difficulties for conventional event detection methodologies.