Browsing by Author "Korkmaz, Gizem"
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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.
- Can Administrative Housing Data Replace Survey Data?Molfino, Emily; Korkmaz, Gizem; Keller, Sallie A.; Schroeder, Aaron; Shipp, Stephanie; Weinberg, Daniel H. (HUD, 2017)This article examines the feasibility of using local administrative data sources for enhancing and supplementing federally collected survey data to describe housing in Arlington County, Virginia. Using real estate assessment data and the American Community Survey (ACS) from 2009 to 2013, we compare housing estimates for six characteristics: number of housing units, type of housing unit, year built, number of bedrooms, housing value, and real estate taxes paid. The findings show that housing administrative data can be repurposed to enhance and supplement the ACS, but limitations exist. We then discuss the challenges of repurposing housing administrative data for research.
- 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.
- Forecasting Social Unrest Using Activity CascadesCadena, Jose; Korkmaz, Gizem; Kuhlman, Christopher J.; Marathe, Achla; Ramakrishnan, Naren; Vullikanti, Anil (PLOS, 2015-06-19)Social unrest is endemic in many societies, and recent news has drawn attention to happenings in Latin America, the Middle East, and Eastern Europe. Civilian populations mobilize, sometimes spontaneously and sometimes in an organized manner, to raise awareness of key issues or to demand changes in governing or other organizational structures. It is of key interest to social scientists and policy makers to forecast civil unrest using indicators observed on media such as Twitter, news, and blogs. We present an event forecasting model using a notion of activity cascades in Twitter (proposed by Gonzalez-Bailon et al., 2011) to predict the occurrence of protests in three countries of Latin America: Brazil, Mexico, and Venezuela. The basic assumption is that the emergence of a suitably detected activity cascade is a precursor or a surrogate to a real protest event that will happen “on the ground.” Our model supports the theoretical characterization of large cascades using spectral properties and uses properties of detected cascades to forecast events. Experimental results on many datasets, including the recent June 2013 protests in Brazil, demonstrate the effectiveness of our approach.
- Philologia, Volume 7 : 2015Marques, Miranda; Hennesey, Darron; Ruckelshaus, Jay; Muelhbauer, Christina; Dagon, Sarah; Goodwin, Michael; Nguyen, Anthony; Graham, Blair; Emsley, Sara; Rhodes, LeAnn; Shepard, Katy; Winfree, Kaitlin; Schneider, Eric; Harlow, Corey; Litvak, Derek; Korkmaz, Gizem; Vaile, Sydney; Lee, Demetria; Gass, Tyler; Homayoun, Tina; Nozick, Daniel (Virginia Tech, 2015)This was a year of firsts for the journal: this issue contains the first non-English piece and the first paper from a student attending a school other than Virginia Tech. In fact, this was the first year Philologia has accepted submissions from outside of our university, and we hope that opening our college’s journal to student work from other universities will help make Philologia increasingly visible and will enhance its national presence. We are proud to continue Philologia’s tradition of interdisciplinarity, publishing work from across the wide range of fields represented in the College of Liberal Arts and Human Sciences.
- Towards an in silico Experimental Platform for Air Quality: Houston, TX as a Case StudyPires, Bianica; Korkmaz, Gizem; Ensor, Katherine; Higdon, David; Keller, Sallie A.; Lewis, Bryan L.; Schroeder, Aaron (CSSSA, 2015)In this paper we couple a spatiotemporal air quality model of ozone concentration levels with the synthetic information model of the Houston Metropolitan Area. While traditional approaches often aggregate the population, activities, or concentration levels of the pollutant across space and/or time, we utilize high performance computing and statistical learning tools to maintain the granularity of the data, allowing us to attach specific exposure levels to the synthetic individuals based on the exact time of day and geolocation of the activity. We demonstrate that maintaining the granularity of the data is critical to more accurately reflect the heterogeneous exposure levels of the population across time within the greater Houston area. We nd that individuals in the same zip code, neighborhood, block, and even household have varying levels of exposure depending on their activity patterns throughout the day.