Browsing by Author "Saraf, Parang"
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- ‘Beating the news’ with EMBERS: Forecasting Civil Unrest using Open Source IndicatorsRamakrishnan, Naren; Butler, Patrick; Self, Nathan; Khandpur, Rupinder P.; Saraf, Parang; Wang, Wei; Cadena, Jose; Vullikanti, Anil Kumar S.; Korkmaz, Gizem; Kuhlman, Christopher J.; Marathe, Achla; Zhao, Liang; Ting, Hua; Huang, Bert; Srinivasan, Aravind; Trinh, Khoa; Getoor, Lise; Katz, Graham; Doyle, Andy; Ackermann, Chris; Zavorin, Ilya; Ford, Jim; Summers, Kristen; Fayed, Youssef; Arredondo, Jaime; Gupta, Dipak; Mares, David; Muthia, Sathappan; Chen, Feng; Lu, Chang-Tien (2014)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 since Nov 2012 which have been (and continue to be) evaluated by an independent T&E team (MITRE). Of note, EMBERS has successfully forecast the uptick and downtick of incidents during the June 2013 protests in Brazil. We outline the system architecture of EMBERS, individual models that leverage specific data sources, and a fusion and suppression engine that supports trading off specific evaluation criteria. EMBERS also provides an audit trail interface that enables the investigation of why specific predictions were made along with the data utilized for forecasting. Through numerous evaluations, we demonstrate the superiority of EMBERS over baserate methods and its capability to forecast significant societal happenings.
- A Cost-Effective Semi-Automated Approach for Comprehensive Event ExtractionSaraf, Parang (Virginia Tech, 2018-04-26)Automated event extraction from free text remains an open problem, particularly when the goal is to identify all relevant events. Manual extraction is currently the only alternative for comprehensive and reliable extraction. Therefore, it is required to have a system that can comprehensively extract events reported in news articles (high recall) and is also scalable enough to handle a large number of articles. In this dissertation, we explore various methods to develop an event extraction system that can mitigate these challenges. We primarily investigate three major problems related to event extraction as follows. (i) What are the strengths and weaknesses of the automated event extractors? A thorough understanding of what can be automated with high success and what leads to common pitfalls is crucial before we could develop a superior event extraction system. (ii) How can we build a hybrid event extraction system that can bridge the gap between manual and automated event extraction? Hybrid extraction is a semi-automated approach that uses an ecosystem of machine learning models along with a carefully designed user interface for extracting events. Since this method is semi-automated it also requires a meticulous understanding of user behavior in order to identify tasks that humans can perform with ease while diverting the more tedious task to the machine learning methods (iii) Finally, we explore methods for displaying extracted events that could simplify the analytical and inference generation processes for an analyst. We particularly aim to develop visualizations that would allow analysts can perform macro and micro level analysis of significant societal events.
- Data analysis and modeling pipelines for controlled networked social science experimentsCedeno-Mieles, Vanessa; Hu, Zhihao; Ren, Yihui; Deng, Xinwei; Contractor, Noshir; Ekanayake, Saliya; Epstein, Joshua M.; Goode, Brian J.; Korkmaz, Gizem; Kuhlman, Christopher J.; Machi, Dustin; Macy, Michael; Marathe, Madhav V.; Ramakrishnan, Naren; Saraf, Parang; Self, Nathan (PLOS, 2020-11-24)There is large interest in networked social science experiments for understanding human behavior at-scale. Significant effort is required to perform data analytics on experimental outputs and for computational modeling of custom experiments. Moreover, experiments and modeling are often performed in a cycle, enabling iterative experimental refinement and data modeling to uncover interesting insights and to generate/refute hypotheses about social behaviors. The current practice for social analysts is to develop tailor-made computer programs and analytical scripts for experiments and modeling. This often leads to inefficiencies and duplication of effort. In this work, we propose a pipeline framework to take a significant step towards overcoming these challenges. Our contribution is to describe the design and implementation of a software system to automate many of the steps involved in analyzing social science experimental data, building models to capture the behavior of human subjects, and providing data to test hypotheses. The proposed pipeline framework consists of formal models, formal algorithms, and theoretical models as the basis for the design and implementation. We propose a formal data model, such that if an experiment can be described in terms of this model, then our pipeline software can be used to analyze data efficiently. The merits of the proposed pipeline framework is elaborated by several case studies of networked social science experiments.