Generating High-Quality Verifiable Relational Data About News Coverage of Social Movement Events

生成有关社会运动事件新闻报道的高质量可验证关系数据

基本信息

批准号：
1918342
负责人：
Pamela Oliver
金额：
$ 38.4万
依托单位：
University of Wisconsin-Madison
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
起止时间：
2019-07-15 至 2022-06-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1918342&HistoricalAwards=false
关键词：
Generating Quality Verifiable Relational Data

项目摘要

Social movements reflect societal concerns with key issues and are important vehicles for tracking social change. Yet activity surrounding specific social movements ebbs and flows. It is important to know what happens when social movements are in the periods of lower mobilization, or abeyance. This project develops a better way of processing electronic archives of articles from minority newspapers to classify stories about social movement events and uses it to collect data on them between 1994 and 2016. This research fills an important knowledge gap because there is no systematic information about minority movement events during this interval. Creating a systematic dataset on events in this period helps us to better understand not only long-term impacts of prior gatherings, but also the contexts from which recent events emerged. These newspaper data supplement existing data already collected from mainstream news wire stories. The events themselves are important for understanding what activists were doing, while the news stories about the events are important for understanding how news media portrayed them and, thus, what the public learned about them. We know that a small number of events receive a great deal of news coverage and have a large effect on public perceptions, while most events are mentioned only once. Comparing mainstream news coverage with that in minority newspapers helps us understand how different groups perceive events differently as well as allowing us to learn about events that are neglected by the mainstream news. Findings from the project will allow both leaders and citizens to better understand social movement trajectories, thus enhancing safety and security as well as political influence in democracies. The dynamics of social movements change over time, with focus often shifting from one topic to another before the original focus reappears. These changes are difficult to detect and verify, particularly using standard news media sources. This project uses a previously develop program to pre-process electronic copies of newspaper articles that have been selected for containing one or more social movement-relevant keywords. It creates new human-machine interfaces and uses relational database structures to expedite the collection and storage of event data from news sources in a way that identifies relations among events and articles about events. A previously-developed interface will be updated to make this work even more efficiently. The project will then develop new coding interfaces. One will match up events between articles; assign each event a unique identifier; recognize and flag relations between events including campaigns, episodes, and master- and sub-event relations; and check the work of coders in marking text in events. The new interface will be linked to a relational database to give the coder access to an authorized list of events, locations, campaigns, episodes, and inter-event relations as well as access to the original full-text articles. In a second coding activity, variables are coded one at a time using automated routines and interfaces appropriate for each variable (e.g. size, issue, form, actor type, police actions). Programming routines will rough-code variables using keyword searches and dictionaries; human coders will make the final coding judgments using variable-specific interfaces. These routines and interfaces will be used to process 7619 articles selected from 121,129 stories in 22 newspapers that were retrieved in February of 2017 from a newspaper archive using our standard set of broad event-relevant words. A database of events identified in the news wires coded under previous funding will be accessible so that newly coded newspaper articles about the same events can be given the same unique identifier. Analysis of the data will combine events from both news wires and minority newspaper sources to give a more complete picture of events and to compare the portrayal of the movement in the two sources. The project will use open source tools and deposit project source code in publicly available archives, thus allowing researchers studying other topics via newspaper articles to make use of newly-developed tools. Project findings will inform sociological theory regarding social movements and social change, particularly regarding how movement dynamics ebb and flow over extended periods of time. More generally, the findings will inform theories of democratic participation, with a focus on status differences in the evolution and change in movement characteristics.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.

社会运动反映了社会对关键问题的关注，是追踪社会变革的重要工具。然而，围绕特定社会运动的活动却有起有落。重要的是要知道当社会运动处于低动员期或暂停期时会发生什么。该项目开发了一种更好的方法来处理少数族裔报纸文章的电子档案，对有关社会运动事件的报道进行分类，并用它来收集 1994 年至 2016 年间的数据。这项研究填补了一个重要的知识空白，因为没有关于少数族裔的系统信息在此间隔期间的运动事件。创建这一时期事件的系统数据集不仅可以帮助我们更好地了解之前聚会的长期影响，还可以更好地了解最近事件发生的背景。这些报纸数据补充了从主流新闻通讯报道中收集的现有数据。事件本身对于了解活动人士在做什么很重要，而有关事件的新闻报道对于了解新闻媒体如何描述这些事件以及公众对事件的了解也很重要。我们知道，少数事件会得到大量新闻报道，对公众看法产生很大影响，而大多数事件只被提及一次。将主流新闻报道与少数派报纸的报道进行比较有助于我们了解不同群体对事件的不同看法，也让我们了解被主流新闻忽视的事件。该项目的调查结果将使领导人和公民更好地了解社会运动轨迹，从而增强民主国家的安全和政治影响力。社会运动的动态会随着时间的推移而发生变化，在最初的焦点重新出现之前，焦点往往会从一个话题转移到另一个话题。这些变化很难检测和验证，特别是使用标准新闻媒体来源。该项目使用先前开发的程序来预处理报纸文章的电子副本，这些文章已被选择为包含一个或多个与社会运动相关的关键词。它创建了新的人机界面，并使用关系数据库结构以识别事件和有关事件的文章之间关系的方式加快从新闻源收集和存储事件数据。先前开发的界面将得到更新，以使这项工作更加高效。然后该项目将开发新的编码接口。将文章之间的事件进行匹配；为每个事件分配一个唯一的标识符；识别并标记事件之间的关系，包括活动、事件以及主事件和子事件关系；并检查编码员在事件中标记文本的工作。新界面将链接到关系数据库，使编码人员能够访问事件、地点、活动、事件和事件间关系的授权列表，以及原始全文文章的访问权限。在第二个编码活动中，使用适合每个变量（例如大小、问题、形式、参与者类型、警察行动）的自动化例程和界面一次对一个变量进行编码。编程例程将使用关键字搜索和字典对变量进行粗略编码；人类编码员将使用特定于变量的接口做出最终的编码判断。这些例程和界面将用于处理 7619 篇文章，这些文章选自 22 份报纸的 121,129 个故事，这些文章是使用我们的标准的广泛事件相关单词集于 2017 年 2 月从报纸档案中检索的。可以访问在先前资助下编码的新闻专线中识别的事件数据库，以便可以为有关相同事件的新编码的报纸文章赋予相同的唯一标识符。对数据的分析将结合来自新闻通讯社和少数派报纸来源的事件，以更全面地了解事件并比较两个来源对运动的描述。该项目将使用开源工具并将项目源代码存放在公开的档案中，从而允许研究人员通过报纸文章研究其他主题，以利用新开发的工具。项目研究结果将为有关社会运动和社会变革的社会学理论提供信息，特别是关于运动动态如何在较长时间内潮起潮落的社会学理论。更一般地说，这些发现将为民主参与理论提供信息，重点关注运动特征演变和变化中的地位差异。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优点和更广泛的影响进行评估，被认为值得支持审查标准。