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

University Of Wisconsin-Madison, Madison WI

Investigators

Abstract

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.