Directed Research Group: The COVID-19 Information Landscape (Fall 2020)

DRG Description

The Covid-19 pandemic has required us to navigate a challenging information landscape. How have our institutions responded, and how have people made sense of the information provided to them? What role have search platforms played in shaping this terrain?

In this Directed Research Group, you'll conduct a content analysis on search engine results collected during the pandemic. The group will be run for 3-5 excellent students interested in engaging in faculty directed research. The research group will be organized by the Community Data Science Collective by Benjamin Mako Hill and Kaylea Champion and will be conducted for UW course credit. We'll analyze the data you collect and reflect on what it can tell us about our response to the crisis.

Prerequisites: Strong reading and writing skills in the English language, a computer you can use during the project, ability to attend team meetings through an online conferencing platform, and a commitment to high-quality results are required. Willingness to work both in a team and independently is required. We strongly prefer candidates with experience in social scientific research methods (such as COM 301). Familiarity with content analysis, R, and Python are all helpful but not required.

Applying: To apply to join this DRG, submit a cover letter and resume to covid-drg@communitydata.science. The resume might contain a brief list of recent courses and any relevant work experience, the cover letter could be a description of why you'd like to participate, including your academic or career interests, or what you hope to learn. If selected, you will be able to enroll for 4-5 credits, depending on how much time you can commit to the project (for 4 credits, you commit 12 hours, for 5 credits, you commit 15 hours per week).

DRG Responsibilities and Commitments

As a DRG member, you are joining an active research project, and you are expected to learn both through information presented to you as well as through your own initiative. What you get out of this project will match what you put into it. Communication is essential to keep our collaboration smooth, and your commitment to doing good quality work is essential. We are trying to develop knowledge that will be useful to the public, and that means holding ourselves to maximum standards of accuracy and ethics. Be honest and open about what you do and always do your best: life happens to all of us and we live in interesting times. Work that's a little late is understandable especially if you communicate early and often, but work that's not your best effort can't be accepted.

DRG Schedule

Week 1:

Key Question: Who are we, what are we trying to do, and how are we going to go about it?

Preparation

• Read pp. 40-60 of Neuendorf

Meeting Agenda

• Introductions
• Scope Review
• Tour and Overview of the Data
• Content Analysis: What is it?
• Commitments and communication

After-Meeting To-Do Items

• Read the project outline.
• Think of at least one question you have about what we're planning to do, and ask it.

Week 2:

Key Question: What are your initial impressions? How can we turn those initial impressions into knowledge?

Meeting Agenda

• Proposal review: what's missing, confusing, or wrong?
• Your sample: what trends did you see in this series of top searches?
• Next sample: Time machine of top trends

After-Meeting Follow-Up

• Visit each item in your sample. Are the results what you expected?
• Read Neuendorff, pp. 62-73, 106-134

Week 3:

Key Question: What is Open Coding and How Will We Do It?

Meeting Agenda

• Content Analysis
• Research Protocol
• Trial Run

After-Meeting Follow-Up

• Read Neuendorff p. 139-166.
• First round open coding

Week 4:

Key Question: Is our approach working? How might we adjust our protocol based on what we're seeing in the data?

Meeting Agenda

• Discuss your experience coding data
• Discuss our agreement levels so far
• Plan next steps

Week 5:

Continue Content Analysis

Week 6:

Continue Content Analysis

Week 7:

Continue Content Analysis; Begin Data Analysis

Week 8:

Continue Data Analysis

Week 9:

Finalize Analysis and Write Up Progress

Week 10:

Complete Writing Tasks

Finals Week:

• Revision Task
• Dissemination Task