Event Detection Based on Semantic Terms from Social Data Stream

Event detection is an interesting area of study that has taken a great deal of attention throughout the last years due to the extensive social media data availability. Problems with event detection have been explored in various social media sources such as Twitter, Flickr, YouTube, and Facebook. The event Detection process includes many challenges, including processing huge volumes of information and high noise levels. The event depends on many basic determinants of which are what, where, and when. Tweets that express a real-world event include semantic terms that match these determinants. The paper presents an incremental approach to clustering based on these determinants and semantic terms that express an event to detect social media events, especially from Twitter. The event detection framework includes three main components: pre-processing, on-line clustering, and event search and detection modules. By implementing some experiments on the proposed framework using a set of large and real-world data, it becomes clear to us that the results are positive. The framework detects events effectively from Twitter compared to other approaches.