Event detection refers to the process of identifying and classifying events or occurrences within a given data set. This research area focuses on developing algorithms and techniques to automatically detect and monitor events in various domains, such as social media, news articles, sensor networks, and financial markets. Event detection involves analyzing large volumes of data in real-time to identify significant changes or abnormalities that may indicate the occurrence of an event. Researchers in this field use machine learning, natural language processing, and data mining techniques to extract relevant information, classify events, and track their evolution over time. Applications of event detection include disaster response, social media monitoring, financial market analysis, and surveillance. Researchers in this field aim to improve the scalability, accuracy, and timeliness of event detection systems to enable timely decision-making and response to critical events.