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Constructing and Validating an Automated Coding System for Electronic News Sources

构建和验证电子新闻来源自动编码系统

基本信息

批准号：
1423784
负责人：
Pamela Oliver
金额：
$ 34.18万
依托单位：
University of Wisconsin-Madison
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2014
资助国家：
美国
起止时间：
2014-08-15 至 2017-07-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1423784&HistoricalAwards=false
关键词：
Constructing Validating Automated Coding System

项目摘要

This project builds, tests, and validates an open-source automated system for coding social movement data from electronically available news sources. Although no source is perfect, scholars agree that the best general source of such data, specifically that on social protest, is a compilation of a large number of news sources. This project draws on the advances in machine learning developed in computer science and statistics and combining them with our deep substantive knowledge as sociologists of the problems of identifying and coding collective action events in news sources. An existing highly-regarded hand-coded data set based on the New York Times is used as the reference for "training" machine learning algorithms -- called "classifiers" -- to recognize elements of an action event in a news article and extract relevant information. We also collect and hand-code new data drawn from other regional, national, and international news sources to provide additional training sets to increase the range and variety of protests we are able to detect. A supplement to the project provides research experience for undergraduates who will be involved in collecting and coding these new data.This project builds, tests, and validates an open-source automated system for coding data from electronically available news sources. It advances the state of data collection in social science and employs the latest developments in natural language processing and supervised machine learning within computer science and statistics. The result will be an open-source, publicly available system that may be used by other researchers and further improved and expanded.This project promises to provide an important new methodological tool of broad interdisciplinary value to social scientists and to open the door to more efficiently compiling collective action-data from news sources that can improve both academic scholarship and public policy. All of the code for this project will be released under an open-source license and publicly accessible through a public source code repository. This work will be accessible and useful to scholars in social movements, international relations, and foreign policy. The work will also be of use to a large number of non-academics, such as foreign policy analysts and decision-makers, journalists, and those interested in computational methods of textual analysis and classification. The ability to code collective action data more efficiently and accurately from news sources has broad policy applicability.

该项目构建、测试和验证一个开源自动化系统，用于对来自电子新闻源的社会运动数据进行编码。虽然没有一个来源是完美的，但学者们一致认为，这类数据的最佳一般来源，特别是关于社会抗议的数据，是大量新闻来源的汇编。该项目借鉴了计算机科学和统计学中机器学习的进步，并将其与我们作为社会学家对识别和编码新闻源中集体行动事件的问题的深刻实质性知识相结合。基于纽约时报的现有高度重视的手工编码数据集被用作“训练”机器学习算法的参考-称为“分类器”-以识别新闻文章中的动作事件的元素并提取相关信息。我们还收集和手工编码来自其他地区，国家和国际新闻来源的新数据，以提供额外的训练集，以增加我们能够检测到的抗议活动的范围和种类。该项目的补充为将参与收集和编码这些新数据的本科生提供了研究经验。该项目构建、测试和验证了一个开源自动化系统，用于对电子新闻源的数据进行编码。它推进了社会科学中数据收集的状态，并采用了计算机科学和统计学中自然语言处理和监督机器学习的最新发展。其结果将是一个开源的，公开可用的系统，可用于其他研究人员和进一步改进和扩展，该项目有望提供一个重要的新的方法工具，广泛的跨学科价值的社会科学家和打开大门，更有效地汇编集体行动的数据，从新闻来源，可以改善学术奖学金和公共政策。该项目的所有代码都将在开源许可证下发布，并通过公共源代码库公开访问。这项工作将是访问和有用的学者在社会运动，国际关系和外交政策。这项工作也将用于大量的非学者，如外交政策分析师和决策者，记者，以及那些有兴趣在文本分析和分类的计算方法。从新闻来源更有效、更准确地编码集体行动数据的能力具有广泛的政策适用性。