Discovering Most Significant News Using Network Science Approach
| Autor: | Vassil Alexandrov, Ilya Blokh |
|---|---|
| Rok vydání: | 2015 |
| Předmět: |
Semantic networks (Information theory)
Social network Computer science business.industry Network science Advertising Supercomputers Public opinion World Wide Web Supercomputadors Xarxes semàntiques (Teoria de la informació) Science network General Earth and Planetary Sciences Popular media High performance computing Mineria de dades business Informàtica::Arquitectura de computadors [Àrees temàtiques de la UPC] Data mining Càlcul intensiu (Informàtica) Period (music) News media General Environmental Science Mass media |
| Zdroj: | ICCS UPCommons. Portal del coneixement obert de la UPC Universitat Politècnica de Catalunya (UPC) |
| ISSN: | 1877-0509 |
| DOI: | 10.1016/j.procs.2015.05.400 |
| Popis: | The role of social network mass media has increased greatly in the recent years. In this paper we investigate news publication in Twitter based on Network Science approach. We analyzed news data posted using the most popular media sources to discover the most significant news over a given period of time. Significance is a qualitative property that reflects the degree of news impact on society and public opinion. We have attempted to define the threshold of significance and discover a number of news which had some significance for society in period of time from July 2014 to January 2015. |
| Databáze: | OpenAIRE |
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