Link-Anomaly Detection in Twitter Streams
International Journal of Science and Research (IJSR)

International Journal of Science and Research (IJSR)
www.ijsr.net | Most Trusted Research Journal Since Year 2012

ISSN: 2319-7064



M.Tech / M.E / PhD Thesis | Computer Science & Engineering | India | Volume 4 Issue 2, February 2015

Link-Anomaly Detection in Twitter Streams

Shari P S

Rapid growth of social network gives emergence to the detection of emerging topics. The information exchanged over social network post not only includes text but also images, URLs and videos therefore conventional frequency based appropriate in this context. By taking into consideration the links between users that are generated dynamically through replies, mentions, and retweets are included. This paper highlights the analysis of a probability model that mention the behavior of a social network user. This model is used to detect the anomalies emerged. From hundreds of users anomaly scores are aggregated. In the proposed system it is only based on replay/mention relationship and is experiment zed with in real datasets gathered from twitter

Keywords: social network, anomaly detection, term-based approach, dynamic threshold optimization, topic detection and tracking

Edition: Volume 4 Issue 2, February 2015

Pages: 1825 - 1828

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How to Cite this Article?

Shari P S, "Link-Anomaly Detection in Twitter Streams", International Journal of Science and Research (IJSR), https://www.ijsr.net/search_index_results_paperid.php?id=SUB151578, Volume 4 Issue 2, February 2015, 1825 - 1828

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