(untitled) (International Journal of Engineering and Computer Science, 2017)
This Project is used to measure to abnormality of future user behavior of users It proposed a possibility model that can capture the normal mentioning behaviour of a user, which consists of both the number of mentions per post and the frequency of users occurring in the mentions. Conventional-term-frequency-based approaches may not be appropriate in this context, because the information exchanged in social-network posts include not only text but also images, URLs, and videos. Our basic assumption is that a new (emerging) topic is something people feel like discussing about, commenting about, or forwarding the information further to their friends. It shows that this approach can detect the emergence of a new topic At leastas fast as using the best term that was not obvious at the moment. Aggregating anomaly scores from hundreds of users, we show that we can detect emerging topics only based on the reply/mention relationships in social-network posts. We demonstrate our technique in several real data sets we gathered from Twitter. The proposed link-anomaly based method can detect the emergence of the topics earlier than keyword frequency based methods.