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-Detection of frequent data is now receiving renewed interest motivated by the rapid growth of social networks.Conventionalterm-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. This system focus on frequent data signalled by social aspects of these networks. Specifically, this system focus on mentions of users links between users that are generated dynamically (intentionally orunintentionally) through replies, mentions, and retweets. Propose a probability model of the mentioning behaviour of a socialnetwork user, and propose to detect the frequent of a data from the anomalies measured through the model. Aggregatinganomaly scores from hundreds of users, shows that can detect frequent data only based on the Scalability of the proposed algorithm, in social-network posts. Demonstrate our technique in several real data sets that gathered from Twitter. The experiments show thatthe proposed mention-anomaly-based approaches can detect new topics at least as early as text-anomaly-based approaches, and insome cases much earlier when the topic is poorly identified by the textual contents in posts

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K.Chandraprabha, S.Santhosh Kumar, M.Srinivasan, & V.Vanitha. (2017). Determining frequent data in social streams using link-anomaly detection . International Journal of Intellectual Advancements and Research in Engineering Computations, 5(1), 853–856. Retrieved from