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Browsing by Author "Muthiah, Sathappan"

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    Design and Maintenance of Event Forecasting Systems
    Muthiah, Sathappan (Virginia Tech, 2021-03-26)
    With significant growth in modern forms of communication such as social media and micro- blogs we are able to gain a real-time understanding into events happening in many parts of the world. In addition, these modern forms of communication have helped shed light into the increasing instabilities across the world via the design of anticipatory intelligence systems [45, 43, 20] that can forecast population level events like civil unrest, disease occurrences with reasonable accuracy. Event forecasting systems are generally prone to become outdated (model drift) as they fail to keep-up with constantly changing patterns and thus require regular re-training in order to sustain their accuracy and reliability. In this dissertation we try to address some of the issues associated with design and maintenance of event forecasting systems in general. We propose and showcase performance results for a drift adaptation technique in event forecasting systems and also build a hybrid system for event coding which is cognizant of and seeks human intervention in uncertain prediction contexts to maintain a good balance between prediction-fidelity and cost of human effort. Specifically we identify several micro-tasks for event coding and build separate pipelines for each with uncertainty estimation capabilities and thereby be able to seek human feedback whenever required for each micro-task independent of the rest.
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    Forecasting Protests by Detecting Future Time Mentions in News and Social Media
    Muthiah, Sathappan (Virginia Tech, 2014-07-11)
    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 simple way to develop a protest forecasting system. We develop such a system in this thesis, 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.