Probabilistic temporal semantic graph: a holistic framework for event detection in twitter

Abstract

Event detection on social media platforms, especially Twitter, poses significant challenges due to the dynamic nature and high volume of data. The rapid flow of tweets and the varied ways users express thoughts complicate the identification of relevant events. Accurately identifying and interpreting events from this noisy and fast-paced environment is crucial for various applications, including crisis management and market analysis. This paper presents a novel unsupervised framework for event detection on social media, designed to enhance the accuracy and efficiency of identifying significant events from Twitter data. The framework incorporates several innovative techniques, including dynamic bandwidth adjustment based on local data density, Mahalanobis distance integration, adaptive kernel density estimation, and an improved Louvain-MOMR method for community detection. Additionally, a new scoring system is implemented to accurately extract trending words that evoke strong emotions, improving the identification of event-related keywords. The proposed framework demonstrates robust performance across three diverse datasets: FACup, Super Tuesday, and US Election, showcasing its effectiveness in capturing temporal and semantic patterns within tweets.