Event detection and user interest discovering in social media data streams

Abstract

Social media plays an increasingly important role in people’s life. Microblogging is a form of social media which allows people to share and disseminate real-life events. Broadcasting events in microblogging networks can be an effective method of creating awareness, divulging important information and so on. However, many existing approaches at dissecting the information content primarily discuss the event detection model and ignore the user interest which can be discovered during event evolution. This leads to difficulty in tracking the most important events as they evolve including identifying the influential spreaders. There is further complication given that the influential spreaders interests will also change during event evolution. The influential spreaders play a key role in event evolution and this has been largely ignored in traditional event detection methods. To this end, we propose a user-interest model based event evolution model, named the HEE (Hot Event Evolution) model. This model not only considers the user interest distribution, but also uses the short text data in the social network to model the posts and the recommend methods to discovering the user interests. This can resolve the problem of data sparsity, as exemplified by many existing event detection methods, and improve the accuracy of event detection. A hot event automatic filtering algorithm is initially applied to remove the influence of general events, improving the quality and efficiency of mining the event. Then an automatic topic clustering algorithm is applied to arrange the short texts into clusters with similar topics. An improved user-interest model is proposed to combine the short texts of each cluster into a long text document simplifying the determination of the overall topic in relation to the interest distribution of each user during the evolution of important events. Finally a novel cosine measure based event similarity detection method is used to assess correlation between events thereby detecting the process of event evolution. The experimental results on a real Twitter dataset demonstrate the efficiency and accuracy of our proposed model for both event detection and user interest discovery during the evolution of hot events.