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The Epidemics of Donations: Logistic Growth and Power-Laws

Overview of attention for article published in PLOS ONE, January 2008
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Title
The Epidemics of Donations: Logistic Growth and Power-Laws
Published in
PLOS ONE, January 2008
DOI 10.1371/journal.pone.0001458
Pubmed ID
Authors

Frank Schweitzer, Robert Mach

Abstract

This paper demonstrates that collective social dynamics resulting from individual donations can be well described by an epidemic model. It captures the herding behavior in donations as a non-local interaction between individual via a time-dependent mean field representing the mass media. Our study is based on the statistical analysis of a unique dataset obtained before and after the tsunami disaster of 2004. We find a power-law behavior for the distributions of donations with similar exponents for different countries. Even more remarkably, we show that these exponents are the same before and after the tsunami, which accounts for some kind of universal behavior in donations independent of the actual event. We further show that the time-dependent change of both the number and the total amount of donations after the tsunami follows a logistic growth equation. As a new element, a time-dependent scaling factor appears in this equation which accounts for the growing lack of public interest after the disaster. The results of the model are underpinned by the data analysis and thus also allow for a quantification of the media influence.

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Mendeley readers

The data shown below were compiled from readership statistics for 57 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Switzerland 2 4%
Turkey 1 2%
Netherlands 1 2%
Singapore 1 2%
Japan 1 2%
United States 1 2%
Unknown 50 88%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 16 28%
Researcher 8 14%
Other 6 11%
Professor 5 9%
Student > Master 5 9%
Other 11 19%
Unknown 6 11%
Readers by discipline Count As %
Social Sciences 13 23%
Engineering 9 16%
Business, Management and Accounting 7 12%
Physics and Astronomy 5 9%
Mathematics 4 7%
Other 11 19%
Unknown 8 14%