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 so...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2015-01-01
|
| Series: | PLoS ONE |
| Online Access: | http://europepmc.org/articles/PMC4595069?pdf=render |
| _version_ | 1818318731776884736 |
|---|---|
| author | Brian J Goode Siddharth Krishnan Michael Roan Naren Ramakrishnan |
| author_facet | Brian J Goode Siddharth Krishnan Michael Roan Naren Ramakrishnan |
| author_sort | Brian J Goode |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-12-13T09:57:53Z |
| format | Article |
| id | doaj.art-694b78447eae41d685763ad83d673689 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-13T09:57:53Z |
| publishDate | 2015-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-694b78447eae41d685763ad83d6736892022-12-21T23:51:44ZengPublic Library of Science (PLoS)PLoS ONE1932-62032015-01-011010e013991110.1371/journal.pone.0139911Pricing a Protest: Forecasting the Dynamics of Civil Unrest Activity in Social Media.Brian J GoodeSiddharth KrishnanMichael RoanNaren RamakrishnanOnline 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.http://europepmc.org/articles/PMC4595069?pdf=render |
| spellingShingle | Brian J Goode Siddharth Krishnan Michael Roan Naren Ramakrishnan Pricing a Protest: Forecasting the Dynamics of Civil Unrest Activity in Social Media. PLoS ONE |
| 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 |
| url | http://europepmc.org/articles/PMC4595069?pdf=render |
| work_keys_str_mv | AT brianjgoode pricingaprotestforecastingthedynamicsofcivilunrestactivityinsocialmedia AT siddharthkrishnan pricingaprotestforecastingthedynamicsofcivilunrestactivityinsocialmedia AT michaelroan pricingaprotestforecastingthedynamicsofcivilunrestactivityinsocialmedia AT narenramakrishnan pricingaprotestforecastingthedynamicsofcivilunrestactivityinsocialmedia |