DocumentCode :
2755931
Title :
Detecting emerging topics and trends via predictive analysis of ‘meme’ dynamics
Author :
Colbaugh, Richard ; Glass, Kristin
Author_Institution :
Sandia Nat. Labs., Albuquerque, NM, USA
fYear :
2011
fDate :
10-12 July 2011
Firstpage :
192
Lastpage :
194
Abstract :
Discovering and characterizing emerging topics and trends through analysis of Web data is of great interest to security analysts and policy makers. This paper considers the problem of monitoring social media to spot emerging memes - distinctive phrases which act as “tracers” for discrete cultural units - as a means of rapidly detecting new topics and trends. We have recently developed a method for predicting which memes will propagate widely and which will not, thereby enabling the discovery of significant topics. Here we demonstrate the efficacy of this approach through case studies involving political memes and memes associated with an emerging cyber threat.
Keywords :
Internet; information analysis; security of data; Web data; cyber threat; discrete cultural units; emerging topics; meme dynamics; predictive analysis; social media monitoring; Blogs; Computational modeling; Glass; Media; Monitoring; Prediction methods; USA Councils; emerging topics; security informatics; social media;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Intelligence and Security Informatics (ISI), 2011 IEEE International Conference on
Conference_Location :
Beijing
Print_ISBN :
978-1-4577-0082-8
Type :
conf
DOI :
10.1109/ISI.2011.5983999
Filename :
5983999
Link To Document :
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