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SUMMARY:Automatic Discovery of Patterns in Media Content - Nello Cristiani
 ni\, Professor of Artificial Intelligence\, Departments of Engineering Mat
 hematics and Computer Science\, University of Bristol
DTSTART:20111123T141500Z
DTEND:20111123T151500Z
UID:TALK32893@talks.cam.ac.uk
CONTACT:Stephen Clark
DESCRIPTION:The strong trend towards the automation of many aspects of sci
 entific enquiry and scholarship has started to affect also the social scie
 nces and even the humanities. Several recent articles have demonstrated th
 e application of pattern analysis techniques to the discovery of non-trivi
 al relations in various datasets that have relevance for social and human 
 sciences\, and some have even heralded the advent of “Computational Soci
 al Sciences” and “Culturomics”. In this review article I survey the 
 results obtained over the past 5 years at the Intelligent Systems Laborato
 ry of Bristol\, in the area of automating the analysis of news media conte
 nt. This endeavor\, which we approach by combining pattern recognition\, d
 ata mining and language technologies\, is traditionally a part of the soci
 al sciences\, and is normally performed by human researchers on small sets
  of data. The analysis of news content is of crucial importance due to the
  central role that the global news system plays in shaping public opinion\
 , markets and culture. It is today possible to freely access online a larg
 e part of global news\, and to devise automated methods for large scale co
 nstant monitoring of patterns in content. The results presented in this su
 rvey show how the automatic analysis of millions of documents in dozens of
  different languages can detect non-trivial macro-patterns that could not 
 be observed at a smaller scale\, and how the social sciences can benefit f
 rom closer interaction with the pattern analysis\, artificial intelligence
  and text mining researchcommunities. 
LOCATION:Lecture Theatre 1\, Computer Laboratory
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