Data mining social networks for evidence of political participation. A demonstration of python being used to data mine the twitter conversations around the #JeSuisCharlie hashtag, and analyzing it to learn about real world protest behavior.
Pinning down the role of social ties in the decision to protest has been notoriously elusive, largely due to data limitations. The era of social media and its global use by protesters offers an unprecedented opportunity to observe real-time social ties and online behavior, though often without an attendant measure of real-world behavior. We collect data on Twitter activity during the 2015 Charlie Hebdo protest in Paris which, unusually, record real-world protest attendance and high-resolution network structure. We draw on a theory of participation in which protest decisions depend on exposure to others' intentions, and network position determines exposure. Our findings are strong and consistent with this theory, showing that, relative to comparable Twitter users, protesters are significantly more connected to one another via direct, indirect, triadic, and reciprocated ties. These results offer the first large-scale empirical support for the claim that social network structure has consequences for protest participation.