Front Inner Page - Volume 3 No.1 February 2016

  • » Back to Index

  • Title

    :

    A Probabilistic Approach for Predicting Anomalies in Social Streams

    Authors

    :

    Abinaya.B1, Chellamaal.P2

    Keywords

    :

    Topic detection, anomaly detection, social networks, sequentially discounted normalized maximum- likelihood coding, burst detection

    Issue Date

    :

    February– 2016

    Abstract

    :

    Basic presumption is that a new (emerging) topic is something people feel like discussing, commenting, or forwarding the information further to their friends. The proposed approach, spot the emergence of topics in a social network stream. It focus on the social aspect of the posts reflected in the mentioning behavior of users instead of the textual contents. A probability model is proposed that captures both the number of mentions per post and the rate of occurrence of mentioned. The detection of emerging topics is the most vital renewed interest in the fast growth of social networks. To detect the anomalies in the social network and the detection is based on the links between the users that are generated dynamically. It has been classified through replies, mentions and retweets. A probability model is used to capture the mentioning behavior of a social network user, and detect the emergence of a new topic from the anomalies measured through the model. It aggregates the anomaly scores based on the reply/mention relationships in social network posts. The real data sets gathered from Twitter and implement using the technique called SDNML, burst detection and Bayesian.

    Page(s)

    :

    1-5

    ISSN

    :

    2347- 4734

    Source

    :

    Vol. 3, No.1, February 2016

    Download

    :


  • » Back index