Paper Info
Title
Planned Protest Modeling In News And Social Media
Abstract
Civil unrest (protests, strikes, and "occupy" events) is a common occurrence in both democracies and authoritarian regimes. The study of civil unrest is a key topic for political scientists as it helps capture an important mechanism by which citizenry express themselves. In countries where civil unrest is lawful, qualitative analysis has revealed that more than 75% of the protests are planned, organized, and/or announced in advance; therefore detecting future time mentions in relevant news and social media is a direct way to develop a protest forecasting system. We develop such a system in this paper, using a combination of key phrase learning to identify what to look for, probabilistic soft logic to reason about location occurrences in extracted results, and time normalization to resolve future tense mentions. We illustrate the application of our system to 10 countries in Latin America, viz. Argentina, Brazil, Chile, Colombia, Ecuador, El Salvador, Mexico, Paraguay, Uruguay, and Venezuela. Results demonstrate our successes in capturing significant societal unrest in these countries with an average lead time of 4.08 days. We also study the selective superiorities of news media versus social media (Twitter, Facebook) to identify relevant tradeoffs.
Year
Venue
Keywords
2015
PROCEEDINGS OF THE TWENTY-NINTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE
logic
Field
DocType
Citations 
Latin Americans,Future tense,Social media,Political economy,Computer science,Authoritarianism,News media,Lead time,Artificial intelligence,Unrest,Politics,Machine learning
Conference
18
PageRank 
References 
Authors
0.74
20
7
Name
Order
Citations
PageRank
Sathappan Muthiah11117.80
Bert Huang256339.09
Jaime Arredondo31014.60
David Mares4862.95
Lise Getoor54365320.21
Graham Katz658941.68
Naren Ramakrishnan71913176.25