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 nowgetting converted interest motivated by therapid growth of social networks. We focuson surfacing of topics signaled by socialaspect of these networks. Specifically, wefocus on mentions of users—link amongusers that are generated energeticallythrough replies, mentions, and retweets. Wepropose a probability model of thementioning performance of a social networkuser, and propose to detect the emergence ofa new topic. We demonstrate our method innumerous real data sets we gathered fromTwitter. The experiments show that theproposed mention – anomaly - basedapproaches can also be detected by newtopics at least as early as text-anomalybasedapproaches, and in some casesgreatly before when the topic is badlyidentified by the textual contents in posts