Higher-Order Singular Value Decomposition Real - Time Bursty Topic Detection From Twitter

Abstract

Twitter becomes one of the largest micro blogging platforms for users around the world. These studies have aimed a extracting the period and the location in which a specified topic frequently occurs. Twitter is the most important and timely source from which people find out and track the breaking news before any mainstream media picks up on them and rebroadcast the footage. A bursty topic in Twitter is one that triggers a surge of relevant tweets within a short period of time, which often reflects important events of mass interest. How to leverage Twitter for early detection of bursty topics has therefore become an important research problem with  immense practical value. Despite the wealth of research work on topic modelling and analysis in Twitter, it remains a challenge to detect bursty topics in real-time. In this paper, we use framework higher-order singular value decomposition (HOSVD) we focus on a system that detects hot topic in a local area and during a particular period. There can be a variation in the words used even though the posts are essentially about the same hot topic. Topic modelling and analysis in Twitter, it remains a challenge to detect bursty topics in real-time. Our experiments on a large Twitter dataset and synthetic datasets show that the proposed models can effectively mine the topic-specific behavioral factors of users and tweet topics. We further demonstrate that the proposed model consistently outperforms the other state-of-the-art content based models in retweet prediction over time