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.

Key Text

Available for free from the UW Library: https://dx-doi-org.offcampus.lib.washington.edu/10.4135/9781071802878

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 the project outline. Come with questions!

Meeting Agenda

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

After-Meeting To-Do Items Read the first two parts of this multipart article about how Google works:

For a more critical view of algorithms and online platforms, please read:

8 https://internethealthreport.org/2019/your-mobile-apps-are-tracking-you/

First sample: Dive In! Write up your thoughts as you look through the two types of data we'll look at: lists of top searches, and images of covid-19 and coronavirus searches.
Read through page 22 of Kaylea's [version of Braun and Clark 2006] -- note that there are both highlighted areas and comments. You may need to adjust your view so you can see the sidebar with comments. Feel free to add your own notes and questions for everyone to benefit.

Week 2:

Key Questions: What are your initial impressions of this data? What do Braun and Clark say about how can we turn those initial impressions into knowledge?

Meeting Agenda

Your sample: what trends did you see? Let's talk about the data!
Walk through Braun and Clark.

After-Meeting Follow-Up

Let's dive into the data and start on Braun and Clark's phase 1 and 2. This is codes development round 1.
Make notes on everything as you go.
Visit each item in your sample for phase 1, and then again for phase 2.
Reflect on what you've seen so far.

Week 3:

Key Question: What are we seeing in this data?

Meeting Agenda

Research Protocol -- what's coming up next?
Open codes discussion: what did you see?

After-Meeting Follow-Up

Braun & Clark phases 1&2 for a new set of data. This is codes development round 2.
Do your previous codes still work? Will you incorporate ideas from colleagues?

Week 4:

Key Question: Is this working? Can we make a codebook out of what we've seen?

Meeting Agenda

Share your codes so far
Codebook production discussion
Prep for next phase.

After-Meeting Follow-Up

Read pp. 1-2, 18-34 of Ch 1 of the online version of Neuendorf

Week 5:

Key Question: Does this codebook make sense?

Meeting Agenda

Codebook presentation, discussion, training.
Synchronized coding + discussion

After-Meeting Follow-Up

Read Neuendorff, Ch 2., pp. 36-44, and Ch 3. pp 70-84. Skim the rest of Ch3.
This is Pilot Round 1, and we are entity coding for agreement

Week 6:

Key Question: Is this working?

Meeting Agenda

Discuss your experience coding data
Discuss our agreement levels so far
Update codebook as needed

After-Meeting Follow-Up

Read Neuendorff p. 139-166.
Second round, entity coding for agreement

Week 7:

Key Question Still Is: Is this working?

Meeting Agenda

Discuss your experience coding data
Discuss our agreement levels so far
Update codebook as needed

Follow-up Items

Read Neuendorff p.169-206
Begin independent coding

Week 8:

Meeting Agenda

Discuss coding progress

Follow-Up Items

Keep Coding
Read Neuendorff p. 221-230, 234-248 (through just the first paragraph)

Week 9

Meeting Agenda

Discuss coding progress

Follow-Up Items

Keep Coding
Read Neuendorf p. 334-340, 394-403

Week 10:

Complete Writing Tasks

Finals Week:

Revision Task
Dissemination Task