DISCOVERING EMERGING TOPICS IN SOCIAL STREAMS VIA LINK ANOMALY DETECTION - P.A, Tintomon.; Krishna, N.Santhana (IJRDO -Journal of Computer Science Engineering, 2016)
Discovery of emerging topics is now getting converted interest motivated by the rapid growth of social networks. We focus on surfacing of topics signaled by social aspect of these networks. Specifically, we focus on mentions of users—link among users that are generated energetically through replies, mentions, and retweets. We propose a probability model of the mentioning performance of a social network user, and propose to detect the emergence of a new topic. We demonstrate our method in numerous real data sets we gathered from Twitter. The experiments show that the proposed mention – anomaly - based approaches can also be detected by new topics at least as early as text-anomalybased approaches, and in some cases greatly before when the topic is badly identified by the textual contents in posts