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A Computational Framework for Multi-Modal Social Action Identification
Preprint   Open access

A Computational Framework for Multi-Modal Social Action Identification

Jason Anastasopoulos and Jake Ryland Williams
arXiv (Cornell University)
24 Oct 2017
DOI: https://doi.org/10.48550/arxiv.1710.07728
url
https://doi.org/10.48550/arxiv.1710.07728View
Preprint (Author's original)arXiv.org - Non-exclusive license to distribute,  Open

Abstract

Computer Science - Computation and Language Computer Science - Computers and Society Computer Science - Social and Information Networks Physics - Physics and Society
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.

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