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Pricing a Protest: Forecasting the Dynamics of Civil Unrest Activity in Social Media
Online social media activity can often be a precursor to disruptive events such as protests, strikes, and “occupy” movements. We have observed that such civil unrest can galvanize supporters through social networks and help recruit activists to their cause. Understanding the dynamics of social netwo...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2015
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4595069/ https://www.ncbi.nlm.nih.gov/pubmed/26441072 http://dx.doi.org/10.1371/journal.pone.0139911 |
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author | Goode, Brian J. Krishnan, Siddharth Roan, Michael Ramakrishnan, Naren |
author_facet | Goode, Brian J. Krishnan, Siddharth Roan, Michael Ramakrishnan, Naren |
author_sort | Goode, Brian J. |
collection | PubMed |
description | Online social media activity can often be a precursor to disruptive events such as protests, strikes, and “occupy” movements. We have observed that such civil unrest can galvanize supporters through social networks and help recruit activists to their cause. Understanding the dynamics of social network cascades and extrapolating their future growth will enable an analyst to detect or forecast major societal events. Existing work has primarily used structural and temporal properties of cascades to predict their future behavior. But factors like societal pressure, alignment of individual interests with broader causes, and perception of expected benefits also affect protest participation in social media. Here we develop an analysis framework using a differential game theoretic approach to characterize the cost of participating in a cascade, and demonstrate how we can combine such cost features with classical properties to forecast the future behavior of cascades. Using data from Twitter, we illustrate the effectiveness of our models on the “Brazilian Spring” and Venezuelan protests that occurred in June 2013 and November 2013, respectively. We demonstrate how our framework captures both qualitative and quantitative aspects of how these uprisings manifest through the lens of tweet volume on Twitter social media. |
format | Online Article Text |
id | pubmed-4595069 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-45950692015-10-09 Pricing a Protest: Forecasting the Dynamics of Civil Unrest Activity in Social Media Goode, Brian J. Krishnan, Siddharth Roan, Michael Ramakrishnan, Naren PLoS One Research Article Online social media activity can often be a precursor to disruptive events such as protests, strikes, and “occupy” movements. We have observed that such civil unrest can galvanize supporters through social networks and help recruit activists to their cause. Understanding the dynamics of social network cascades and extrapolating their future growth will enable an analyst to detect or forecast major societal events. Existing work has primarily used structural and temporal properties of cascades to predict their future behavior. But factors like societal pressure, alignment of individual interests with broader causes, and perception of expected benefits also affect protest participation in social media. Here we develop an analysis framework using a differential game theoretic approach to characterize the cost of participating in a cascade, and demonstrate how we can combine such cost features with classical properties to forecast the future behavior of cascades. Using data from Twitter, we illustrate the effectiveness of our models on the “Brazilian Spring” and Venezuelan protests that occurred in June 2013 and November 2013, respectively. We demonstrate how our framework captures both qualitative and quantitative aspects of how these uprisings manifest through the lens of tweet volume on Twitter social media. Public Library of Science 2015-10-06 /pmc/articles/PMC4595069/ /pubmed/26441072 http://dx.doi.org/10.1371/journal.pone.0139911 Text en © 2015 Goode et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. |
spellingShingle | Research Article Goode, Brian J. Krishnan, Siddharth Roan, Michael Ramakrishnan, Naren Pricing a Protest: Forecasting the Dynamics of Civil Unrest Activity in Social Media |
title | Pricing a Protest: Forecasting the Dynamics of Civil Unrest Activity in Social Media |
title_full | Pricing a Protest: Forecasting the Dynamics of Civil Unrest Activity in Social Media |
title_fullStr | Pricing a Protest: Forecasting the Dynamics of Civil Unrest Activity in Social Media |
title_full_unstemmed | Pricing a Protest: Forecasting the Dynamics of Civil Unrest Activity in Social Media |
title_short | Pricing a Protest: Forecasting the Dynamics of Civil Unrest Activity in Social Media |
title_sort | pricing a protest: forecasting the dynamics of civil unrest activity in social media |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4595069/ https://www.ncbi.nlm.nih.gov/pubmed/26441072 http://dx.doi.org/10.1371/journal.pone.0139911 |
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