Computational Modeling of Grievances and Political Instability through Global Media
Gary LaFree (Professor of Criminology and Criminal Justice; Director, START)
George Mohler (Assistant Professor of Computer Science and Mathematics)
David Cunningham (Associate Professor of Government and Politics)
Jennifer Golbeck (Associate Professor and Director, Social Intelligence Lab)
Paul Torrens (Professor, Center of Urban Science and Progress)
National Consortium for Study of Terrorism and Responses to Terrorism (START)
University of Maryland
The project was funded through a grant of the National Science Foundation awarded to the National Consortium for the Study of Terrorism and Responses to Terrorism (START) at the University of Maryland. The aim of the project was to investigate whether grievances expressed on social media can predict political instability and conflict.
The project used social media and web media sources to analyze the impact of perceived grievances on levels of unrest within countries, with a special emphasis on election related violence. The goal was to develop a more accurate and comprehensive understanding of the relationship between perceived grievances and political instability that could in future allow to better predict both violent and non-violent instability events.
The research built on an extensive literature examining the relationship between grievances and political instability. Much of this research, however, has been hampered by the relative absence of data on individual level sentiment that can be assessed in (near) real time. Harnessing the availability of individual-level data on human sentiments gathered from social media, the project aims to triangulate perceptions of grievance across a multilingual archive of billions of tweets and hundreds of millions of news articles and use these to construct a novel proxy of perceived, issue-specific grievances.
The substantive focus of the project was on election-related political instability in Africa, a region where having accurate estimates of where and when political instability and conflict are likely to emerge could be especially valuable.