Military-Funded Study Predicts When You’ll Protest on Twitter


It’s no simple problem for the obvious reason that the telltale heart beats but for the guilty. “The ways in which protest-related events affect a person are not observable, resulting in a lack of knowledge of factors operating at that time causing his next post to be a declaration of protest,” the researchers write in their study, published as part of the proceedings of the Association for the Advancement of Artificial Intelligence conference earlier this month.

The researchers collected 2,686 posts related to the Nigerian general election that took place between February and April of last year, an election that was marred by political violence in the form of the Boko Haram insurgency and that was beset by accusations of voting irregularities. So what predicts when someone begins protesting on Twitter? It’s not your personal history so much as your Twitter history of interacting with people who are part of that movement.

“The interaction we study is how users mention each other,” researchers Suhas Ranganath and Fred Morstatter wrote to Defense One in an email. “In the model, the probability of the future post expressing protest increases if: 1) The post mentioning the user is related to the protest. 2) The author of the post mentioning the user is interested in the protest. We dynamically learn [or teach] the model by testing how each of the previous status messages of the given user are affected by the recent posts mentioning him. We then use the model to predict the likelihood of the user expressing protest in his next post.”

Their accuracy threshold of 70 percent is because what might seem completely unpredictable is in fact part of a pattern, albeit one that’s incredibly complex. The researchers employed Brownian motion theory to design the formula, a theory that usually is employed to track the movement of particles, as well as model stock market fluctuations and other highly complicated systems. “Brownian Motion for fluid particles models change in the direction of the particle movement  on collision with other particles. We take each ‘particle’ as a social media user. We relate collision with other particles, other users mentioning him, and the change in direction to change of the user’s inclination to express protest in his next post. We then use the models of Brownian motion to relate the two quantities. We mainly employ this to model the dynamic change in user behavior resulting from interactions over time,” Suhas told Defense One.

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