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Erschienen in: Social Network Analysis and Mining 1/2016

01.12.2016 | Original Article

Predicting charitable donations using social media

verfasst von: Rostyslav Korolov, Justin Peabody, Allen Lavoie, Sanmay Das, Malik Magdon-Ismail, William Wallace

Erschienen in: Social Network Analysis and Mining | Ausgabe 1/2016

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Abstract

We study the relationship between chatter on social media and observed actions concerning charitable donation. One hypothesis is that a fraction of those who act will also tweet about it, implying a linear relation. However, if the contagion is present, we expect a superlinear scaling. We consider two scenarios: donations in response to a natural disaster, and regular donations. We empirically validate the model using two location-paired sets of social media and donation data, corresponding to the two scenarios. Results show a quadratic relation between chatter and action in emergency response case. In case of regular donations, we observe a near-linear relation. Additionally, regular donations can be explained by demographic factors, while for a disaster response social media is a much better predictor of action. A contagion model is used to predict the near-quadratic scaling for the disaster response case. This suggests that diffusion is present in emergency response case, while regular charity does not spread via social network. Understanding the scaling behavior that relates social media chatter to physical actions is an important step in estimating the extent of a response and for determining social media strategies to affect the response.

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Fußnoten
2
The Contact and Excitation models are similar to the threshold and independent cascade models in influence propagation. The main difference is that in our process, the propagation stops after one step.
 
3
For sparse graphs, \(q=O(\frac{1}{|V|})\) and for dense graphs, \(q=O(1).\)
 
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Metadaten
Titel
Predicting charitable donations using social media
verfasst von
Rostyslav Korolov
Justin Peabody
Allen Lavoie
Sanmay Das
Malik Magdon-Ismail
William Wallace
Publikationsdatum
01.12.2016
Verlag
Springer Vienna
Erschienen in
Social Network Analysis and Mining / Ausgabe 1/2016
Print ISSN: 1869-5450
Elektronische ISSN: 1869-5469
DOI
https://doi.org/10.1007/s13278-016-0341-1

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