Browsing by Author "Self, Nathan"

Now showing 1 - 6 of 6
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    ‘Beating the news’ with EMBERS: Forecasting Civil Unrest using Open Source Indicators
    Ramakrishnan, 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.
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    Citation Forecasting with Multi-Context Attention-Aided Dependency Modeling
    Ji, Taoran; Self, Nathan; Fu, Kaiqun; Chen, Zhiqian; Ramakrishnan, Naren; Lu, Chang-Tien (ACM, 2024)
    Forecasting citations of scientific patents and publications is a crucial task for understanding the evolution and development of technological domains and for foresight into emerging technologies. By construing citations as a time series, the task can be cast into the domain of temporal point processes. Most existing work on forecasting with temporal point processes, both conventional and neural network-based, only performs single-step forecasting. In citation forecasting, however, the more salient goal is n-step forecasting: predicting the arrival of the next n citations. In this paper, we propose Dynamic Multi-Context Attention Networks (DMA-Nets), a novel deep learning sequence-to-sequence (Seq2Seq) model with a novel hierarchical dynamic attention mechanism for long-term citation forecasting. Extensive experiments on two real-world datasets demonstrate that the proposed model learns better representations of conditional dependencies over historical sequences compared to state-of-the-art counterparts and thus achieves significant performance for citation predictions.
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    Data analysis and modeling pipelines for controlled networked social science experiments
    Cedeno-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.
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    Redistrict: Designing a Self-Serve Interactive Boundary Optimization System
    Sistrunk, Andreea; Self, Nathan; Biswas, Subhodip; Luther, Kurt; Verdezoto, Nervo; Ramakrishnan, Naren (ACM, 2023-07-10)
    The assignment of parcels of land afects many communal activities, from voting to public school assignments. This process creates unease and often has a strong impact on communities. We propose Redistrict, an interactive web-based system designed to support redistricting deliberations for public school zoning. Redistrict helps community members explore and experiment with the possible consequences of various zoning scenarios. This point-and-click digital discovery activity allows the user to understand long-term implications of proposed zonings and to provide feedback in an easy, intuitive way. By providing the opportunity for more people, individually or collectively, to look at the problem from diferent points of view, Redistrict promotes transparency, shared understanding, and cooperation. We designed Restrict to serve as a common information space to help cultivate trust and enable communities to grow stronger, smarter, and more resilient.
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    Redistrict: Online Public Deliberation Support that Connects and Rebuilds Inclusive Communities
    Sistrunk, Andreea; Self, Nathan; Biswas, Subhodip; Luther, Kurt; Diaz Verdezoto, Nervo; Ramakrishnan, Naren (ACM, 2024-04-23)
    Public deliberations are often a staple ingredient in community decision-making. However, traditional, time-constrained, in-person debates can become highly polarized, eroding trust in authorities, and leaving the community divided. This is the case in redistricting deliberations for public school zoning. Seeking alternative ways of support, we evaluated the potential introduction of an online platform that combines multiple streams of data, visualizes school attendance boundaries, and enables the manipulation of representations of land parcels. To capture multiple stakeholders’ values about the potential to enhance public engagement in school rezoning decision-making through an online platform, we conducted interviews with 12 participants with previous experiences in traditional, in-person deliberations. Insights from the interviews highlight the several roles an online platform could take, especially as it provides alternative means of participation (online, synchronous, and asynchronous). Additionally, we discuss the potential for technology to increase the visibility and participation of multiple community actors in public deliberations and present implications for the design of future tools to support public decision-making.
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    User Interfaces for an Open Source Indicators Forecasting System
    Self, Nathan (Virginia Tech, 2015-10-05)
    Intelligence analysts today are faced with many challenges, chief among them being the need to fuse disparate streams of data and rapidly arrive at analytical decisions and quantitative predictions for use by policy makers. A forecasting tool to anticipate key events of interest is an invaluable aid in helping analysts cut through the chatter. We present the design of user interfaces for the EMBERS system, an anticipatory intelligence system that ingests myriad open source data streams (e.g., news, blogs, tweets, economic and financial indicators, search trends) to generate forecasts of significant societal-level events such as disease outbreaks, protests, and elections. A key research issue in EMBERS is not just to generate high-quality forecasts but provide interfaces for analysts so they can understand the rationale behind these forecasts and pose why, what-if, and other exploratory questions. This thesis presents the design and implementation of three visualization interfaces for EMBERS. First, we illustrate how the rationale behind forecasts can be presented to users through the use of an audit trail and its associated visualization. The audit trail enables an analyst to drill-down from a final forecast down to the raw (and processed) data sources that contributed to the forecast. Second, we present a forensics tool called Reverse OSI that enables analysts to investigate if there was additional information either in existing or new data sources that can be used to improve forecasting. Unlike the audit trail which captures the transduction of data from raw feeds into alerts, Reverse OSI enables us to posit connections from (missed) forecasts back to raw feeds. Finally, we present an interactive machine learning approach for analysts to steer the construction of machine learning mod-els. This provides fine-grained control into tuning tradeoffs underlying EMBERS. Together, these three interfaces support a range of functionality in EMBERS, from visualization of algorithm output to a complete framework for user feedback via a tight human-algorithm loop. They are currently being utilized by a range of user groups in EMBERS: analysts, social scientists, and machine learning developers, respectively.