  We create a computational framework for understanding social action and
demonstrate how this framework can be used to build an open-source event
detection tool with scalable statistical machine learning algorithms and a
subsampled database of over 600 million geo-tagged Tweets from around the
world. These Tweets were collected between April 1st, 2014 and April 30th,
2015, most notably when the Black Lives Matter movement began. We demonstrate
how these methods can be used diagnostically-by researchers, government
officials and the public-to understand peaceful and violent collective action
at very fine-grained levels of time and geography.

Social and Information Networks;Computation and Language;Computers and Society;Physics and Society