Event Detection in Twitter using Aggressive Filtering and Hierarchical Tweet Clustering
|Title:||Event Detection in Twitter using Aggressive Filtering and Hierarchical Tweet Clustering||Authors:||Ifrim, Georgiana
|Permanent link:||http://hdl.handle.net/10197/7546||Date:||8-Apr-2014||Online since:||2016-04-05T09:00:55Z||Abstract:||Twitter has become as much of a news media as a social network, and much research has turned to analysing its content for tracking real-world events, from politics to sports and natural disasters. This paper describes the techniques we employed for the SNOW Data Challenge 2014, described in . We show that aggressive lettering of tweets based on length and structure, combined with hierarchical clustering of tweets and ranking of the resulting clusters, achieves encouraging results. We present empirical results and discussion for two different Twitter streams focusing on the US presidential elections in 2012 and the recent events about Ukraine, Syria and the Bitcoin, in February 2014.||Funding Details:||Science Foundation Ireland||Type of material:||Conference Publication||Publisher:||ACM||Copyright (published version):||2014 ACM||Keywords:||Event detection; Twitter; Social media; Digital journalism; News aggregation||Other versions:||http://www.snow-workshop.org/2014/||Language:||en||Status of Item:||Peer reviewed||Conference Details:||Second Workshop on Social News on the Web (SNOW), Seoul, Korea, 8 April 2014|
|Appears in Collections:||Insight Research Collection|
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