"Making sense of it all": an attempt to aid journalists in analysing and filtering user generated content

Authors:
Sotiris Diplaris;Symeon Papadopoulos;Ioannis Kompatsiaris;Nicolaus Heise;Jochen Spangenberg;Nic Newman;Hakim Hacid
Affiliations:
Informatics and Telematics Institute, Thessaloniki, Greece;Informatics and Telematics Institute, Thessaloniki, Greece;Informatics and Telematics Institute, Thessaloniki, Greece;Deutsche Welle Strategy, Marketing & Distribution / New Media, Berlin, Germany;Deutsche Welle Strategy, Marketing & Distribution / New Media, Berlin, Germany;School of Arts - Journalism, City University London, London, United Kingdom;Bell Labs France, Alcatel-Lucent, Nozay, France
Venue:
Proceedings of the 21st international conference companion on World Wide Web
Year:
2012

Citing 7
Cited 0

Social influence analysis in large-scale networks

Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
GeoFolk: latent spatial semantics in web 2.0 social media

Proceedings of the third ACM international conference on Web search and data mining
Reliable peers and useful resources: Searching for the best personalised learning path in a trust- and recommendation-aware environment

Information Sciences: an International Journal
Earthquake shakes Twitter users: real-time event detection by social sensors

Proceedings of the 19th international conference on World wide web
Discovery of super-mediators of information diffusion in social networks

DS'10 Proceedings of the 13th international conference on Discovery science
Modeling Information Diffusion in Implicit Networks

ICDM '10 Proceedings of the 2010 IEEE International Conference on Data Mining
Cluster-Based Landmark and Event Detection for Tagged Photo Collections

IEEE MultiMedia

Quantified Score

Hi-index	0.00

Visualization

Abstract

This position paper explores how journalists can embrace new ways of content provision and authoring, by aggregating and analyzing content gathered from Social Media. Current challenges in the news media industry are reviewed and a new system for capturing emerging knowledge from Social Media is described. Novel features that assist professional journalists in processing sheer amounts of Social Media information are presented with a reference to the technical requirements of the system. First implementation steps are also discussed, particularly focusing in event detection and user influence identification.