Peering Into the Data Divide: Part III
Peering Into the Data Divide is a mini-series of conversations between Zameena and her peers about their individual processes of learning interactive journalism and, in turn, closing the data divide that many journalists convince themselves they can’t get over.
Everic White (‘16)
Hometown: Bronx, NY
Concentration: Urban Reporting
Fun Fact: I definitely put on PBS documentaries at night to fall asleep to.
ZM: Tell us a bit about The O-Bomb. What first inspired you to start it? How did you know to look at debate transcripts?
EW: The O-Bomb project was actually a joke at first. When watching the 2012 GOP debate cycle, a friend and I had a watch party drinking game where you’d have to have a drink every time President Obama was mentioned. During this election cycle, when candidates kept mentioning Obama, I realized it would be a good project to examine how much they did and the implications. I knew to look at debate transcripts, because trying to tally O-Bombs live was difficult. Using the transcripts, I could examine the debates at my own leisure and I knew I’d have an exact record of everything said.
ZM: What was it like researching this topic? Did you find similar stories/graphics made to inspire you? Provide links to any examples.
EW: The research to do this project was easy, considering all of the transcripts go live the day after the debates. I didn’t have much in the way of other news stories to inspire me. The book Manufacturing Consent by Noam Chomsky, though, gave a a great framework for how to approach my project. Chomsky essentially measures how the United States news media differs in its coverage of “worthy and “unworthy” issues, using statistics to back up how well or poorly they were covered. From that, I was better able to parse what constituted an O-Bomb and what didn’t.
ZM: How did you learn about the tools (ie, the data scraping program you used) used to gather your data?
EW: I learned about the tool that I used to scrape the debates, Outwit Hub, during a January Academy class on data scraping. It couldn’t have come at a better time. From Sandeep’s instruction, I was easily able to use Outwit Hub to find each negative Obama mention and any other negative references of him. That still didn’t stop me from manually going through the transcripts though. Sometimes, data scraping can be as rudimentary as using pen to mark up a print out of a debate transcript.
ZM: Why did you choose to present the information the way that you did? Are there any improvements you want to make to it?
EW: I chose to present the data graphically, mostly because it was the only medium I have a lot of experience in creating visualizations for. I thought of trying to do a graph for each candidate, but the contrast between, for example, Ted Cruz and John Kasich, was so great, that it wouldn’t make for a compelling visual. Also, I originally was going to use Obama’s head as a unit of measurement, but had the better sense to display the data more seriously. Improvement-wise, I’m concerned about adding the last few debates to a chart. Also, I’m thinking about examining the O-Bomb from the Democratic side; perhaps there’s a more positive spin there.
ZM: How much time did it take to complete the graphic component? How long did it take to put the story all together?
EW: I had been watching all the debates all along, but I didn’t start the project until early December. After I compiled all of the data from the transcripts, creating the graphic component took a while. I’m used to using Excel and ChartBuilder for graphics, but decided on using Adobe Illustrator because of how much customization you can do with it. It took the better part of two evenings to create the graphic, mostly because I was watching Homeland at the same time, too LOL. The story itself was a quick write-up, since I’d been looking at the numbers for so long. I focused mostly on the outliers (Cruz, Rubio, Christie) and the front-runner (Trump) among the data, so it was an easy story to write at that point. After the 2nd Jan debate, it took me about three days total to do the scraping, the chart, and the write-up.
ZM: Did your current internship influence/help you complete this project in any way?
EW: My current internship really didn’t have an influence on the project, aside from being a place where I could watch the debates with other journalists AND without illegally streaming it.
Check out The O-Bomb for a complete look at Everic’s research!
Interested in being featured in the Peering Into the Data Divide mini-series? Submit your request over at http://bit.ly/cdjsubmissions.
