Event Tracking and Analysis in Social Text Stream

Gigantic growth of Internet and social networking is directly associated with handheld devices which offers direct access and easy updates to social user to be a part of the wisdom crowd in different circumstances. Such updates help users to know about whereabouts. Social users participate in numerous events using various social networking platforms without knowing the history of an event. In this way some events becomes so popular and few demise after a certain period. Detection of event, classification and tracking plays a vital role in social media text streams. Tracking social event and novelty detection is often interesting and challenging task due to huge participation of social users. Exact analysis and tracking of event is very important and needy task in various research domains such as start of event, source of an event’s location, time and user profile in social media analysis. But, as the social media is growing, its nature is also changing and becoming complex to review. Therefore, the main objective of this paper is to study and analyze the various social media events including novelty detection and tracking. We provide a vivid taxonomy for event analysis and tracking including resource utilization in terms of memory and CPU in huge text stream. Various SOTA approaches for event tracking and novelty detection in social media are compared with proposed approach using Twitter text stream including future research directions.