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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

The xs, xi phase plane showing a trajectory.The box on the left of the diagram is the target set, 𝒯, to which the state is directed. The function, V(x), measures the cost to go from any x to 𝒯. V(x) is decreasing along trajectories as t increases. The disturbance control, d, needed to guide the trajectory to 𝒯 can be found using these functions.
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pone.0139911.g002: The xs, xi phase plane showing a trajectory.The box on the left of the diagram is the target set, 𝒯, to which the state is directed. The function, V(x), measures the cost to go from any x to 𝒯. V(x) is decreasing along trajectories as t increases. The disturbance control, d, needed to guide the trajectory to 𝒯 can be found using these functions.

Mentions: As shown in Fig 2, the solution to this equation consists of finding an optimal disturbance control, d*, and a corresponding value function, V(x(t)), such that V(x(t)) = J(x, t, d*). The value function represents the β€œcost-to-go” for reaching the target set, 𝒯 using control, d*. The target set and boundary conditions are identified as,βˆ€xβˆˆπ’―βŠ‚X,V(x)=c0(8)where 𝒯 is a set of target states that are assigned the terminal cost, c0. In the case of information spreading in a social network, the target set for a population trying to reach the most number of users is 𝒯 = {x ∈ X∣xs = 0}, shown in Fig 2. From Eq (4), this terminal condition assigns the target as reaching the entire N count population. Implicit in the preceding analysis are assumptions of (i) no finite end time of the cascade or outside intervention on the population level (i.e., individual participation only) and (ii) agents operating rationally with full knowledge per the cost structure. The first assumption is addressed by setting c0, so the state remains in the target set upon entry. The second assumption differentiates between cascades displaying different cost features given below.


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

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

The xs, xi phase plane showing a trajectory.The box on the left of the diagram is the target set, 𝒯, to which the state is directed. The function, V(x), measures the cost to go from any x to 𝒯. V(x) is decreasing along trajectories as t increases. The disturbance control, d, needed to guide the trajectory to 𝒯 can be found using these functions.
© Copyright Policy
Related In: Results  -  Collection

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

pone.0139911.g002: The xs, xi phase plane showing a trajectory.The box on the left of the diagram is the target set, 𝒯, to which the state is directed. The function, V(x), measures the cost to go from any x to 𝒯. V(x) is decreasing along trajectories as t increases. The disturbance control, d, needed to guide the trajectory to 𝒯 can be found using these functions.
Mentions: As shown in Fig 2, the solution to this equation consists of finding an optimal disturbance control, d*, and a corresponding value function, V(x(t)), such that V(x(t)) = J(x, t, d*). The value function represents the β€œcost-to-go” for reaching the target set, 𝒯 using control, d*. The target set and boundary conditions are identified as,βˆ€xβˆˆπ’―βŠ‚X,V(x)=c0(8)where 𝒯 is a set of target states that are assigned the terminal cost, c0. In the case of information spreading in a social network, the target set for a population trying to reach the most number of users is 𝒯 = {x ∈ X∣xs = 0}, shown in Fig 2. From Eq (4), this terminal condition assigns the target as reaching the entire N count population. Implicit in the preceding analysis are assumptions of (i) no finite end time of the cascade or outside intervention on the population level (i.e., individual participation only) and (ii) agents operating rationally with full knowledge per the cost structure. The first assumption is addressed by setting c0, so the state remains in the target set upon entry. The second assumption differentiates between cascades displaying different cost features given below.

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