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Topical Anomaly Detection from Twitter Stream

Title Topical Anomaly Detection from Twitter Stream
Author , ,
Location Association for Computing Machinery Web Science
Year 2012
Resource Type Short Paper
Keyword(s) Anomaly detection,spam,off-topic content detection,binary classification,twitter stream analysis
Full Citation Pramod Anantharam, Krishnaprasad Thirunarayan, and Amit Sheth, 'Topical Anomaly Detection from Twitter Stream', Research Note: In the Proceedings of ACM Web Science 2012, Evanston, Illinois, pp. 11-14 June 22-24, 2012.
Abstract In this paper, we address the problem of finding topically anomalous tweets in twitter streams by analyzing the content of the document pointed to by the URLs in the tweets in preference to the textual content of the tweet. Existing approaches ignore such URLs thereby missing additional opportunities to detect off-topic tweets. Specifically, we determine the divergence of claimed topic of a tweet as reflected by the hashtags and the actual topic as reflected by the document content. Our approach avoids the need for labeled samples by selecting documents from reliable sources gleaned from the URLs present in the tweets. These documents are used for comparison against documents from unknown URLs in incoming tweets improving both scalability and adaptability to rapidly changing topics. We evaluate our approach on three events and show that it can find topical inconsistencies not detectable by existing approaches.
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