What news matters?
Gabriel Lenz, Professor
Political Science
Applications for Spring 2025 are closed for this project.
This project has two parts.
Part 1 looks at how the news media shapes policy outcomes and whether disparities in coverage lead to policy inequity. Can we show convincing evidence that news coverage of pedestrian fatalities leads to a change in local transportation policy? Are certain communities more likely to receive coverage and thus, receive better policy interventions? To answer these questions, we have collected news coverage of pedestrian fatalities from Lexis Nexis. We are currently developing a way to match this coverage to a rich dataset of pedestrian fatality data collected by the federal government.
Part 2 looks at news consumption within the federal government. In particular, it looks at news summaries produced for Presidents Clinton, Bush, and Obama. Can we show that the news that reaches the president is unrepresentative? What drives news selection? What are the consequences of political elites viewing news media? Does local news have a national audience?
Role: We need several undergraduate research assistants to help categorize our set of news articles. Undergraduates will learn how to to categorize the type of coverage and extract attributes that can be used to match articles with the database of pedestrian fatalities. Students may also be asked to assist with other research tasks including collecting data, literature reviews, or editing draft papers.
We also have several tasks appropriate for undergraduates with prior training in text analysis and machine learning. In particular, undergrads with appropriate skills may be asked to assist with Natural Language Processing tasks related to parsing news coverage into separate incidents, extracting victim and incident characteristics, and matching news coverage to a federal database of pedestrian fatalities. Assistance may also be required with parsing and analyzing the contents of news summaries. As a result of this assistance, undergraduates will learn how to apply technical skills to social science questions.
Qualifications: See above section for details.
(Required) Attention to detail and excellent communication skills. Familiarity with spreadsheets.
(Optional) Experience with text analysis, machine learning, web scraping, APIs, R, Python.
Day-to-day supervisor for this project: Julia Christensen, Ph.D. candidate
Hours: 3-5 hrs
Social Sciences