title: Discovering social events through online attention.
abstract: Twitter is a major social media platform in which users send and read messages ("tweets") of up to 140 characters. In recent years this communication medium has been used by those affected by crises to organize demonstrations or find relief. Because traffic on this media platform is extremely heavy, with hundreds of millions of tweets sent every day, it is difficult to differentiate between times of turmoil and times of typical discussion. In this work we present a new approach to addressing this problem. We first assess several possible "thermostats" of activity on social media for their effectiveness in finding important time periods. We compare methods commonly found in the literature with a method from economics. By combining methods from computational social science with methods from economics, we introduce an approach that can effectively locate crisis events in the mountains of data generated on Twitter. We demonstrate the strength of this method by using it to locate the social events relating to the Occupy Wall Street movement protests at the end of 2011.
authors: Center for Polymer Studies and Department of Physics, Boston University, Boston, Massachusetts, United States of America.
authors: Dror Y
authors: DY
authors: Kenett
authors: School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, Arizona, United States of America.
authors: Fred
authors: F
authors: Morstatter
authors: Center for Polymer Studies and Department of Physics, Boston University, Boston, Massachusetts, United States of America.
authors: H Eugene
authors: HE
authors: Stanley
authors: School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, Arizona, United States of America.
authors: Huan
authors: H
authors: Liu
mesh_terms: D001288:Attention; D006801:Humans; D020407:Internet / Q000191:economics / Q000706:statistics & numerical data*; D009519:New York City; D012919:Social Behavior