Rupinder Paul Khandpur

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Modeling the movement of information within social media outlets, like Twitter, is key to understanding to how ideas spread but quantifying such movement runs into several difficulties. Two specific areas that elude a clear characterization are (i) the intrinsic random nature of individuals to potentially adopt and subsequently broadcast a Twitter topic,(More)
Model Based Event Recognition using Surrogates (EMBERS) is a large-scale big data analytics system for forecasting significant societal events, such as civil unrest events on the basis of continuous, automated analysis of large volumes of publicly available data. It has been operational since November 2012 and delivers approximately 50 predictions each day(More)
We describe the design, implementation, and evaluation of EMBERS, an automated, 24x7 continuous system for forecasting civil unrest across 10 countries of Latin America using open source indicators such as tweets, news sources, blogs, economic indicators, and other data sources. Unlike retrospective studies, EMBERS has been making forecasts into the future(More)
Introduction Spatial computing is now widely pervasive in the engineering and science disciplines but we argue that there is an even bigger revolution happening in our ability to comprehend human behavior. Modern geo-tagged communication forms such as social media and microblogs are rapidly advancing the methods by which we can comprehend, and even(More)
—Developed under the IARPA Open Source Initiative program, EMBERS (Early Model Based Event Recognition using Surrogates) is a large-scale big-data analytics system for forecasting significant societal events, such as civil unrest incidents and disease outbreaks on the basis of continuous, automated analysis of large volumes of publicly available data. It(More)
EMBERS is an anticipatory intelligence system forecasting population-level events in multiple countries of Latin America. A deployed system from 2012, EMBERS has been generating alerts 24x7 by ingesting a broad range of data sources including news, blogs, tweets, machine coded events,currency rates, and food prices. In this paper, we describe our(More)
Recovery funding from disasters is a complex system of cooperation between formal and informal stakeholders. Network analysis can shed light into the underlying mechanisms that occur during the post-disaster recovery phase. In this study, we apply a data-driven approach on online news articles and other publicly available information about the 1989 Loma(More)
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