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.

社会运动反映了关键问题的社会问题，并且是跟踪社会变革的重要工具。然而，围绕特定社会运动的活动消退。重要的是要知道，当社会运动在较低的动员或暂停时期发生时会发生什么。该项目开发了一种更好的方法来处理从少数报纸的文章电子档案，以对社会运动事件进行分类，并在1994年至2016年之间使用它来收集有关它们的数据。这项研究填补了重要的知识差距，因为在此间隔内没有有关少数民族运动事件的系统信息。在此期间，创建有关事件的系统数据集有助于我们更好地理解先前聚会的长期影响，还可以更好地了解最近事件出现的环境。这些报纸数据补充已经从主流新闻电线故事中收集的现有数据。事件本身对于了解激进分子的工作很重要，而有关这些事件的新闻报道对于理解新闻媒体如何描绘它们以及公众对他们的知识很重要。我们知道，少数事件获得了很多新闻报道，并且对公众看法产生了很大的影响，而大多数事件仅提及一次。将主流新闻报道与少数族裔报纸中的报道进行比较，有助于我们了解不同的群体如何对事件有所不同，并允许我们了解主流新闻忽略的事件。该项目的发现将使领导人和公民能够更好地理解社会运动的轨迹，从而增强安全和保障以及在民主国家的政治影响力。社会运动的动态随着时间的流逝而变化，在原始重点重新出现之前，重点经常从一个主题转移到另一个主题。这些更改很难检测和验证，尤其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，这些副本已被选为包含一个或多个与社会运动相关的关键字。它创建了新的人机接口，并使用关系数据库结构以确定事件和有关事件的文章之间的关系的方式加快新闻来源的事件数据的收集和存储。先前开发的界面将进行更新，以使此工作更加有效。然后，该项目将开发新的编码接口。一个将匹配文章之间的事件；为每个事件分配一个唯一的标识符；认识和标记事件之间的关系，包括运动，情节以及主要和次事实关系；并在事件中的标记文本中检查编码器的作品。新界面将链接到关系数据库，以使编码员访问授权的事件，位置，广告系列，情节和事件间关系，并访问原始的全文文章。在第二次编码活动中，使用适合每个变量的自动例程和接口（例如大小，问题，表单，表格，参与者类型，警察行动）一次编码变量。编程例程将使用关键字搜索和词典进行粗编码变量；人类编码人员将使用可变特异性接口做出最终的编码判断。这些例程和界面将用于处理从22个报纸中的121,129个故事中选择的7619篇文章，这些文章是在2017年2月使用我们标准的一套与事件相关的广泛词的标准档案中从报纸档案中检索的。可以访问根据先前资金编码的新闻电线中确定的事件数据库，以便可以将有关相同事件的新编码报纸文章给予相同的唯一标识符。对数据的分析将结合新闻电线和少数报纸来源的事件，以提供更完整的事件图片，并比较两个来源中运动的刻画。该项目将使用开源工具并将项目源代码存入公开档案中，从而使研究人员通过报纸文章研究其他主题，可以利用新开发的工具。项目发现将为社会运动和社会变革提供信息，尤其是关于运动动态如何在长时间内消退的方式。更一般而言，这些发现将为民主参与的理论提供依据，重点关注运动特征的进化和变化的地位差异。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子和更广泛影响的评估评估标准来通过评估来支持的。