Local agenda setting, gatekeeping bias in local news media, and elite news consumption
Gabriel Lenz, Professor
Political Science
Closed. This professor is continuing with Spring 2024 apprentices on this project; no new apprentices needed for Fall 2024.
This project looks at how the news media shapes policy outcomes and whether disparities in coverage lead to policy inequity. It has several parts.
1) 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.
2) What news is consumed by elite actors who can directly influence policy outcomes? Has the availability of search tools like Google News Alerts changed the content or sources of media consumed by elites? To answer these questions, we will be analyzing daily news summaries produced for the White House from 1993 to 2016.
Role: Undergraduate roles will depend on their individual skill sets and interests.
We have several tasks appropriate for undergraduates with prior training in text analysis and machine learning. In particular, undergrads 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.
Undergraduates with some prior training in text analysis and machine learning will be asked to work with the supervisors to develop additional skills as needed while learning how to apply technical skills to social science questions.
Undergraduates with few technical skills will be given tasks that align with their interests. Students who are interested in acquiring further technical skills will be given tasks designed to build desired skills. Students who are not interested in developing technical skills may be assigned to code training data, collect and categorize published research, or edit draft papers.
Qualifications: See above section for details.
(Required) Attention to detail and excellent communication skills. Familiarity with spreadsheets.
(Desirable) 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