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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

Precision for the Brazilian data set is given as a function of k.As expected, the more observations available for a given cascade, the better forecasting ability as seen by an increasing average across each of the clusters. Convergence is seen toward 80% precision.
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pone.0139911.g011: Precision for the Brazilian data set is given as a function of k.As expected, the more observations available for a given cascade, the better forecasting ability as seen by an increasing average across each of the clusters. Convergence is seen toward 80% precision.

Mentions: The dataset for the Brazilian protests is analyzed for varying lengths, k, of the cascades starting from the initial tweet. The precision, shown in Fig 11, shows increases from k = 1000 to k = 11000. We see similar results for both recall and the weighted F1 score shown in Figs 12 and 13 respectively. These metrics show substantial improvement over both the sample frequency base rate and temporal features. One of the primary reasons that the temporal features do not forecast membership of a cascade to a particular cluster as well is that the elapsed time temporal feature over-fits the cascades. When only a portion of the cascades are seen, the clusters become indistinguishable. The cost captures more of the cascade dynamics, and discriminates at an earlier stage of cascade growth. This effect only becomes more exaggerated as k is increased to include more cascade volume in the forecast.


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

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

Precision for the Brazilian data set is given as a function of k.As expected, the more observations available for a given cascade, the better forecasting ability as seen by an increasing average across each of the clusters. Convergence is seen toward 80% precision.
© Copyright Policy
Related In: Results  -  Collection

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

pone.0139911.g011: Precision for the Brazilian data set is given as a function of k.As expected, the more observations available for a given cascade, the better forecasting ability as seen by an increasing average across each of the clusters. Convergence is seen toward 80% precision.
Mentions: The dataset for the Brazilian protests is analyzed for varying lengths, k, of the cascades starting from the initial tweet. The precision, shown in Fig 11, shows increases from k = 1000 to k = 11000. We see similar results for both recall and the weighted F1 score shown in Figs 12 and 13 respectively. These metrics show substantial improvement over both the sample frequency base rate and temporal features. One of the primary reasons that the temporal features do not forecast membership of a cascade to a particular cluster as well is that the elapsed time temporal feature over-fits the cascades. When only a portion of the cascades are seen, the clusters become indistinguishable. The cost captures more of the cascade dynamics, and discriminates at an earlier stage of cascade growth. This effect only becomes more exaggerated as k is increased to include more cascade volume in the forecast.

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