A first look at global news coverage of disasters by using the GDELT dataset
In this work, we reveal the structure of global news coverage of disasters and its determinants by using a large-scale news coverage dataset collected by the GDELT (Global Data on Events, Location, and Tone) project that monitors news media in over 100 languages from the whole world. Significant var...
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2014
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sg-smu-ink.sis_research-70692021-09-29T13:11:04Z A first look at global news coverage of disasters by using the GDELT dataset KWAK, Haewoon AN, Jisun. In this work, we reveal the structure of global news coverage of disasters and its determinants by using a large-scale news coverage dataset collected by the GDELT (Global Data on Events, Location, and Tone) project that monitors news media in over 100 languages from the whole world. Significant variables in our hierarchical (mixed-effect) regression model, such as population, political stability, damage, and more, are well aligned with a series of previous research. However, we find strong regionalism in news geography, highlighting the necessity of comprehensive datasets for the study of global news coverage. 2014-11-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/6066 info:doi/10.1007/978-3-319-13734-6_22 https://ink.library.smu.edu.sg/context/sis_research/article/7069/viewcontent/Kwak_An2014_Chapter_AFirstLookAtGlobalNewsCoverage.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University GDELT global news coverage news geography regionalism theory of newsworthiness international news agency foreign news disaster Numerical Analysis and Scientific Computing |
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GDELT global news coverage news geography regionalism theory of newsworthiness international news agency foreign news disaster Numerical Analysis and Scientific Computing |
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GDELT global news coverage news geography regionalism theory of newsworthiness international news agency foreign news disaster Numerical Analysis and Scientific Computing KWAK, Haewoon AN, Jisun. A first look at global news coverage of disasters by using the GDELT dataset |
| description |
In this work, we reveal the structure of global news coverage of disasters and its determinants by using a large-scale news coverage dataset collected by the GDELT (Global Data on Events, Location, and Tone) project that monitors news media in over 100 languages from the whole world. Significant variables in our hierarchical (mixed-effect) regression model, such as population, political stability, damage, and more, are well aligned with a series of previous research. However, we find strong regionalism in news geography, highlighting the necessity of comprehensive datasets for the study of global news coverage. |
| format |
text |
| author |
KWAK, Haewoon AN, Jisun. |
| author_facet |
KWAK, Haewoon AN, Jisun. |
| author_sort |
KWAK, Haewoon |
| title |
A first look at global news coverage of disasters by using the GDELT dataset |
| title_short |
A first look at global news coverage of disasters by using the GDELT dataset |
| title_full |
A first look at global news coverage of disasters by using the GDELT dataset |
| title_fullStr |
A first look at global news coverage of disasters by using the GDELT dataset |
| title_full_unstemmed |
A first look at global news coverage of disasters by using the GDELT dataset |
| title_sort |
first look at global news coverage of disasters by using the gdelt dataset |
| publisher |
Institutional Knowledge at Singapore Management University |
| publishDate |
2014 |
| url |
https://ink.library.smu.edu.sg/sis_research/6066 https://ink.library.smu.edu.sg/context/sis_research/article/7069/viewcontent/Kwak_An2014_Chapter_AFirstLookAtGlobalNewsCoverage.pdf |
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1770575807188566016 |