Cascade properties as predictors of protest.

<p>Cascade size, number of users, and number of cascades for Follower and MRT cascades in Brazil for the period November 2012—June 2013.</p

Forecasting Social Unrest Using Activity Cascades

<div><p>Social unrest is endemic in many societies, and recent news has drawn attention to happenings in Latin America, the Middle East, and Eastern Europe. Civilian populations mobilize, sometimes spontaneously and sometimes in an organized manner, to raise awareness of key issues or to demand changes in governing or other organizational structures. It is of key interest to social scientists and policy makers to forecast civil unrest using indicators observed on media such as Twitter, news, and blogs. We present an event forecasting model using a notion of activity cascades in Twitter (proposed by Gonzalez-Bailon et al., 2011) to predict the occurrence of protests in three countries of Latin America: Brazil, Mexico, and Venezuela. The basic assumption is that the emergence of a suitably detected activity cascade is a precursor or a surrogate to a real protest event that will happen “on the ground.” Our model supports the theoretical characterization of large cascades using spectral properties and uses properties of detected cascades to forecast events. Experimental results on many datasets, including the recent June 2013 protests in Brazil, demonstrate the effectiveness of our approach.</p></div

Node and shell removal heuristics for CSSP (Venezuela).

<p>Here, we see the largest remaining sub-cascade size in terms of numbers of tweets (normalized by the original size) as a function of numbers of remaining nodes in the cascade graph (normalized by the original number of nodes). This cascade occurred in April 2013, and its original size is 226,179 tweets.</p

Descriptive statistics of selected features (Brazil) for the MRT and F models.

<p>The names in the first column consist of the name of the structural feature (i.e., cascade size, duration or slope, which is the incremental increase in the size per day), and the statistical operations (i.e. median, average etc.).</p

ROC curve for Brazil.

<p>ROC curve for different models for Brazil, for a training period of Nov 2012 through May 2013 and testing period of June 1-30, 2013.</p

Number of tweets for the period May 2012 to Nov 2013.

<p>Number of tweets for the period May 2012 to Nov 2013.</p

Formation of cascades in the Twitter follower network.

<p>At time <i>t</i>, node 1 posts a tweet. Nodes 2 and 4 post at times <i>t</i>₂ and <i>t</i>₄ between <i>t</i> and <i>t</i>′ = <i>t</i> + <i>D</i>. Node 5, which follows 2, posts at some time <i>t</i>₅ between <i>t</i>′ and <i>t</i>″ = <i>t</i>′ + <i>D</i>. Therefore, the cascade <i>C</i>(1, <i>t</i>, <i>D</i>) is <i>C</i>(1, <i>t</i>, <i>D</i>) = {(1, <i>t</i>), (2, <i>t</i>₂), (4, <i>t</i>₄), (5, <i>t</i>₅)}.</p

ROC curves for the baseline model.

<p>We show the ROC curves for Mexico, Brazil, and Venezuela. Training period November 1, 2012 to November 9, 2013; test period November 10, 2013 to November 30, 2013.</p

Node and shell removal heuristics for CSSP (Venezuela).

<p>Here, we see the largest remaining sub-cascade size in terms of numbers of tweets (normalized by the original size) as a function of numbers of remaining nodes in the cascade graph (normalized by the original number of nodes). This cascade occurred in April 2013, and its original size is 226,179 tweets.</p

Performance of the predictive models.

<p>We show the performance of the three models in terms of accuracy, brier score, and area under the ROC curve. The cascades model has the best performance accross different countries.</p