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Pricing a Protest: Forecasting the Dynamics of Civil Unrest Activity in Social Media.

Goode BJ, Krishnan S, Roan M, Ramakrishnan N - PLoS ONE (2015)

Bottom Line: But factors like societal pressure, alignment of individual interests with broader causes, and perception of expected benefits also affect protest participation in social media.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.

View Article: PubMed Central - PubMed

Affiliation: Discovery Analytics Center, Virginia Tech, Arlington, VA, United States of America; Dept. of Mechanical Engineering, Virginia Tech, Blacksburg, VA, United States of America.

ABSTRACT
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.

No MeSH data available.


Related in: MedlinePlus

Five randomly selected cascades were chosen from each cluster to illustrate their appearance and behavior.One of the reasons we see that high linear correlation in the cluster diagram is that many of the clusters exhibit similar behavior. For protest cascades in Brazil, this means that the cascades start strong and weaken as time increases, exhibiting less SIR growth dynamics. As the cascades grow, less SIR dynamics are seen, giving them a higher cost and strong linear relationship to tweet size.
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pone.0139911.g009: Five randomly selected cascades were chosen from each cluster to illustrate their appearance and behavior.One of the reasons we see that high linear correlation in the cluster diagram is that many of the clusters exhibit similar behavior. For protest cascades in Brazil, this means that the cascades start strong and weaken as time increases, exhibiting less SIR growth dynamics. As the cascades grow, less SIR dynamics are seen, giving them a higher cost and strong linear relationship to tweet size.

Mentions: The cluster results show unique differences between the cascade behaviors of the two protests. A random sampling of cascades from both protests are shown in Fig 9 for Brazil and Fig 10 for Venezuela. The color scheme of the cascades corresponds to the representative clusters in Figs 7 and 8. The behavior emerging in the clusters is verified by the shape of the resulting cascade time series. For Brazil, the clusters show varying degrees of tweet volume, but each instance shows an initial behavior with epidemic growth in tweet volume. These cascades then decrease accruing tweet volume as their growth progresses. The Venezuelan data set shows more similarity in tweet volume, but differences in behavior are seen in the rates at which the cascades accrue volume.


Pricing a Protest: Forecasting the Dynamics of Civil Unrest Activity in Social Media.

Goode BJ, Krishnan S, Roan M, Ramakrishnan N - PLoS ONE (2015)

Five randomly selected cascades were chosen from each cluster to illustrate their appearance and behavior.One of the reasons we see that high linear correlation in the cluster diagram is that many of the clusters exhibit similar behavior. For protest cascades in Brazil, this means that the cascades start strong and weaken as time increases, exhibiting less SIR growth dynamics. As the cascades grow, less SIR dynamics are seen, giving them a higher cost and strong linear relationship to tweet size.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4595069&req=5

pone.0139911.g009: Five randomly selected cascades were chosen from each cluster to illustrate their appearance and behavior.One of the reasons we see that high linear correlation in the cluster diagram is that many of the clusters exhibit similar behavior. For protest cascades in Brazil, this means that the cascades start strong and weaken as time increases, exhibiting less SIR growth dynamics. As the cascades grow, less SIR dynamics are seen, giving them a higher cost and strong linear relationship to tweet size.
Mentions: The cluster results show unique differences between the cascade behaviors of the two protests. A random sampling of cascades from both protests are shown in Fig 9 for Brazil and Fig 10 for Venezuela. The color scheme of the cascades corresponds to the representative clusters in Figs 7 and 8. The behavior emerging in the clusters is verified by the shape of the resulting cascade time series. For Brazil, the clusters show varying degrees of tweet volume, but each instance shows an initial behavior with epidemic growth in tweet volume. These cascades then decrease accruing tweet volume as their growth progresses. The Venezuelan data set shows more similarity in tweet volume, but differences in behavior are seen in the rates at which the cascades accrue volume.

Bottom Line: But factors like societal pressure, alignment of individual interests with broader causes, and perception of expected benefits also affect protest participation in social media.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.

View Article: PubMed Central - PubMed

Affiliation: Discovery Analytics Center, Virginia Tech, Arlington, VA, United States of America; Dept. of Mechanical Engineering, Virginia Tech, Blacksburg, VA, United States of America.

ABSTRACT
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.

No MeSH data available.


Related in: MedlinePlus