课题基金 / 基金详情

Elements: Data: Sustaining Modern Infrastructure For Political And Social Event Data

Elements: Data: Sustaining Modern Infrastructure For Political And Social Event Data
要素:数据:维持政治和社会事件数据的现代基础设施
批准号:
1931541
负责人:
Patrick Brandt
金额:
$58.8万
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2019
资助国家:
美国
项目状态:
已结题
起止时间:
2019-10-01 至 2023-09-30

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中文摘要
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英文摘要
This project extracts quantitative summaries of political and international conflict events among national and non-state actors across the world by combing news reports across the internet in multiple languages. This generates event data, a machine-coded description of someone doing something to someone else as extracted from news reports. The project focuses on political and social events about conflict and cooperation between governments, individuals, non-governmental organizations, rebel groups, and others. The main goal of this project is to integrate and expand the end-to-end cyberinfrastructure for the robust creation, validation, access, and analysis of political event data by national security, government, academic, and non-governmental actors. A major component of this proposal is to continue to grow the project's engagement with the global event data community. This project extends, produces, and integrates a dynamic, robust system for event data to study sub-national and international conflict processes at a global scale, with applications to the needs of the national security and intelligence communities. Using natural language processing software tools to code event data by annotating the kinds of political events that are of interest to political scientists, international relations scholars, sociologists, and the national security community, the project analyzes contemporaneous news reports in English and Spanish, automatically encodes relevant political events for data analysts, and serves the data along with other open event data via the project websites. The technical challenges include: (1) additional extensions of the multilingual framework to more types of events; (2) smoother updates to political actor dictionaries; (3) robust data querying and linking mechanisms, and analytic tools for the broader research and user community; (4) improved methods for focus location extraction across languages and resolutions. This will improve event data quality and event detection through increased, multi-language comparisons. The multi-lingual extensions of the event encoding software and interface will produce novel methods for detecting and analyzing rare and local events. The proposed database integrations and query optimizations will streamline access to the many open access event datasets that exist, enabling researchers across diverse communities to analyze and compare conflict and political processes. The refinements of the geolocation modules will allow detection of locations from biased training samples, which is an important advancement since some political events, such as human rights violations, tend to occur in locations with low news coverage. The robust and innovative geolocation approaches can be carried over to other domain applications. Scaling the related software and data infrastructure aids the political science, national security and big data research communities. We also will provide robust data linkages across a diverse set of event data from multiple and multilingual news reporting services. The sustainable cyberinfrastructure not only includes the event data coding from news reports, but also analysis tools that include an R package and a thin-client browser-based analysis interface to the data. This sustains the cyberinfrastructure and creates a workforce that is able to work in both science, engineering, national security, and intelligence.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
期刊论文(10)
专著(0)
科研奖励(0)
会议论文
Forecasting conflict in Africa with automated machine learning systems
使用自动化机器学习系统预测非洲冲突
DOI: 10.1080/03050629.2022.2017290
发表时间: 2022
期刊: International Interactions
影响因子: 1.3
作者: [D’Orazio, Vito, Lin, Yu]
通讯作者: Lin, Yu
DOI: 10.1109/dsaa49011.2020.00055
发表时间: 2020-10
期刊: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA)
影响因子: --
作者: [Eric Parolin;L. Khan;Javier Osorio;Vito D'Orazio;Patrick T. Brandt;J. Holmes]
通讯作者: Eric Parolin;L. Khan;Javier Osorio;Vito D'Orazio;Patrick T. Brandt;J. Holmes
CoMe-KE: A New Transformers Based Approach for Knowledge Extraction in Conflict and Mediation Domain
CoMe-KE:一种基于 Transformers 的冲突与调解领域知识提取新方法
DOI: 10.1109/bigdata52589.2021.9672080
发表时间: 2021
期刊: IEEE
影响因子: --
作者: [Parolin, Erick Skorupa, Hu, Yibo, Khan, Latifur, Osorio, Javier, Brandt, Patrick T., D'Orazio, Vito]
通讯作者: D'Orazio, Vito
DOI: 10.1109/bigdata55660.2022.10020509
发表时间: 2022-12
期刊: 2022 IEEE International Conference on Big Data (Big Data)
影响因子: --
作者: [Eric Parolin;Yibo Hu;Latif Khan;Patrick T. Brandt;Javier Osorio;Vito D'Orazio]
通讯作者: Eric Parolin;Yibo Hu;Latif Khan;Patrick T. Brandt;Javier Osorio;Vito D'Orazio
10
    Frameworks: Infrastructure For Political And Social Event Data using Machine Learning
    • 批准号:
      2311142
    • 项目类别:
      Standard Grant
    • 资助金额:
      $158.9万
    • 财政年份:
      2023
    • 负责人:
      Patrick Brandt
    • 依托单位:
    RIDIR: Modernizing Political Event Data for Big Data Social Science Research
    • 批准号:
      1539302
    • 项目类别:
      Standard Grant
    • 资助金额:
      $149.74万
    • 财政年份:
      2015
    • 负责人:
      Patrick Brandt
    • 依托单位:
    Collaborative Research: Development of a Technology for Real Time, Ex Ante Forecasting of Intra and International Conflict and Cooperation
    • 批准号:
      0921051
    • 项目类别:
      Standard Grant
    • 资助金额:
      $24.53万
    • 财政年份:
      2009
    • 负责人:
      Patrick Brandt
    • 依托单位:
    Collaborative Research: Bayesian Time Series Models for the Analysis of International Conflict
    • 批准号:
      0540816
    • 项目类别:
      Standard Grant
    • 资助金额:
      $0.0万
    • 财政年份:
      2005
    • 负责人:
      Patrick Brandt
    • 依托单位:
    国内基金
    海外基金
    Scalable Learning and Optimization: High-dimensional Models and Online Decision-Making Strategies for Big Data Analysis
    Data-driven Recommendation System Construction of an Online Medical Platform Based on the Fusion of Information
    Development of a Linear Stochastic Model for Wind Field Reconstruction from Limited Measurement Data
    • 批准号:
      --
    • 项目类别:
      --
    • 资助金额:
      40万元
    • 批准年份:
      2020
    • 负责人:
      Vikrant Gupta
    • 依托单位:
    基于Linked Open Data的Web服务语义互操作关键技术
    • 批准号:
      61373035
    • 项目类别:
      面上项目
    • 资助金额:
      77.0万元
    • 批准年份:
      2013
    • 负责人:
      冯志勇
    • 依托单位: