@article {muthiah:aimag16, title = {Capturing Planned Protests from Open Source Indicators}, journal = {AI Mag}, volume = {37}, number = {2}, year = {2016}, pages = {63{\textendash}75}, abstract = {

Civil unrest events (protests, strikes, and {\textquotedblleft}occupy{\textquotedblright} events) are common occurrences 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 percent of the protests are planned, organized, or announced in advance; therefore detecting references to future planned events in relevant news and social media is a direct way to develop a protest forecasting system. We report on a system for doing that in this article. It uses a combination of keyphrase learning to identify what to look for, probabilistic soft logic to reason about location occurrences in extracted results, and time normalization to resolve future time mentions. We illustrate the application of our system to 10 countries in Latin America: 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 trade-offs.

}, author = {Sathappan Muthiah and Bert Huang and Jaime Arredondo and David Mares and Lise Getoor and Graham Katz and Naren Ramakrishnan} } @conference {kumar:asonam16, title = {Unsupervised Models for Predicting Strategic Relations between Organizations}, booktitle = {ASONAM}, year = {2016}, abstract = {

Microblogging sites like Twitter provide a platform for sharing ideas and expressing opinions. The widespread popularity of these platforms and the complex social structure that arises within these communities provides a unique opportunity to understand the interactions between users. The political domain, especially in a multi-party system, presents compelling challenges, as political parties have different levels of alignment based on their political strategies. We use Twitter to understand the nuanced relationships between differing political entities in Latin America. Our model incorporates diverse signals from the content of tweets and social context from retweets, mentions and hashtag usage. Since direct communications between entities are relatively rare, we explore models based on the posts of users who interact with multiple political organizations. We present a quantitative and qualitative analysis of the results of models using different features, and demonstrate that a model capable of using sentiment strength, social context, and issue alignment has superior performance to less sophisticated baselines.

}, author = {Shachi Kumar and Jay Pujara and Lise Getoor and David Mares and Dipak Gupta and Ellen Riloff} }