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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)
Characterizing information diffusion on social platforms like Twitter enables us to understand the properties of underlying media and model communication patterns. As Twitter gains in popularity, it has also become a venue to broadcast rumors and misinformation. We use epidemiological models to characterize information cascades in twitter resulting from… (More)
Financial markets are quite sensitive to unanticipated news and events. Identifying the effect of news on the market is a challenging task. In this demo, we present Forex-foreteller (FF) which mines news articles and makes forecasts about the movement of foreign currency markets. The system uses a combination of language models, topic clustering, and… (More)
Topic modeling techniques have been widely used to uncover dominant themes hidden inside an unstructured document collection. Though these techniques first originated in the probabilistic analysis of word distributions, many deep learning approaches have been adopted recently. In this paper, we propose a novel neural network based architecture that produces… (More)
With the rapid rise in social media, alternative news sources, and blogs, ordinary citizens have become information producers as much as information consumers. Highly charged prose, images, and videos spread virally, and stoke the embers of social unrest by alerting fellow citizens to relevant happenings and spurring them into action. We are interested in… (More)
We introduce a system for visual analysis of news articles and emails. This system was developed in response to VAST MiniChallenge 1 and comprises different interfaces for mining textual data and network data.
We describe the EMBERS AutoGSR system that conducts automated coding of civil unrest events from news articles published in multiple languages. The nuts and bolts of the AutoGSR system constitute an ecosystem of filtering, ranking, and recommendation models to determine if an article reports a civil unrest event and, if so, proceed to identify and encode… (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)
We introduce a system for visual analysis of news articles, emails, GPS tracking data, financial transactions and streaming micro-blog data. This system was developed in response to the 2014 VAST Grand Challenge and comprises of several interfaces for mining textual, network, spatio-temporal, financial, and streaming data.
We introduce a system for visualization and analysis of geo-spatial and temporal data from call center and microblog sources. We provide a streaming client for interacting with the data in real time. We present the data in a partitioned format using coordinated visualizations that allows the analyst to view the data in multiple dimension simultaneously.… (More)