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Social Network Analysis and Mining
Published in:

01-12-2016 | Original Article

Predicting charitable donations using social media

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

Published in: Social Network Analysis and Mining | Issue 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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Footnotes
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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Metadata
Title
Predicting charitable donations using social media
Authors
Rostyslav Korolov
Justin Peabody
Allen Lavoie
Sanmay Das
Malik Magdon-Ismail
William Wallace
Publication date
01-12-2016
Publisher
Springer Vienna
Published in
Social Network Analysis and Mining / Issue 1/2016
Print ISSN: 1869-5450
Electronic ISSN: 1869-5469
DOI
https://doi.org/10.1007/s13278-016-0341-1

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