Detecting Attention Dominating Moments Across Media Types
|Title:||Detecting Attention Dominating Moments Across Media Types||Authors:||Brigadir, Igor
|Permanent link:||http://hdl.handle.net/10197/8365||Date:||20-Mar-2016||Abstract:||In this paper we address the problem of identifying attention dominating moments in online media. We are interested in discovering moments when everyone seems to be talking about the same thing. We investigate one particular aspect of breaking news: the tendency of multiple sources to concentrate attention on a single topic, leading to a collapse in diversity of content for a period of time. In this work we show that diversity at a topic level is effective for capturing this effect in blogs, in news articles, and on Twitter. The phenomenon is present in three distinctly different media types, each with their own unique features. We describe the phenomenon using case studies relating to major news stories from September 2015.||Funding Details:||Science Foundation Ireland||Type of material:||Conference Publication||Publisher:||CEUR Workshop Proceedings||Keywords:||Machine learning;Statistics||Language:||en||Status of Item:||Peer reviewed||Is part of:||Martinez, M., Kruschwitz, U., Kazai, G., Corney, D., Hopfgartner, F., Campos, R., Albakour, D. (eds.). Proceedings of the NewsIR’16 Workshop at ECIR||Conference Details:||NewsIR’16 Workshop at ECIR, Padua, Italy, 20-March 2016|
|Appears in Collections:||Insight Research Collection|
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