The other month, we asked readers why they did or did not own guns. Here’s the post on the creation of that. We ended up getting over 1,500 responses, and our readers were largely thoughtful in what they wrote. Before we launched this project, we didn’t know how our audience would break down into gun owners and non-gun owners, and whether we would get the usual talking points from each side or hear new points of view. We fortunately did see a lot of interesting stories and patterns. But uncovering those trends in over a thousand responses in only a few days was a challenge. Reading through every response on deadline was unfeasible, so we had a machine do it.
The idea of using natural language processing to make sense of a large number of reader comments came from Blair Hickman, Social Media Producer at ProPublica and we ended up using the Overview Project a natural language processing tool developed by the Associated Press (screenshotted above). You can read more about the technicals of how it works here but generally, it looks for clusters of related words and groups them together in that tree layout you see.
Some interesting trends (Read the analysis with selected comments) that surfaced were a group of comments about how growing up with guns influenced people to both own and not own guns later in life. Many people framed their non-ownership of guns around their lack of a need and the algorithm found this cluster around phrases with “need” in them. Viewed in this light, many gun owner responses can be seen as implicitly responding to this prompt, explaining their clear, pragmatic need for gun ownership such as hunting, protection in rural areas or many others we included. Overview also brought out repeated comments that had variations on the phrase “When seconds count, police are minutes away,” a similar need for self-protection or a desire for self-reliance, depending on how you interpret it.
A number of readers discussed how their association with the military sometimes led them to be comfortable with firearms or, in many cases, the opposite. As one person put it: “As an army officer I came to realize many guns are tools created with a main purpose of killing. I don’t hunt and I don’t target shoot. No need for a gun.”
The clustering algorithm also grouped comments related to how experiences with family members committing suicide led people to not own guns, as well as religion. The posts concerning religion were especially interesting since in 2008 campaign speech, President Obama said that some people “cling to guns or religion” as a way to “explain their frustrations” with society. All of the comments submitted to us that discussed religion were from staunch non-gun owners, however.
I think the post is worth a read if you’re interested in seeing an attempt at surfacing a conversation. On the design side, you can see we tried to get out of the way of the readers’ voices as much as possible: we wrote a short sentence intro framing each collection of comments and then let them tell their stories. Our front-end dev wiz Lynn Maharas actually pushed through that blue background quote style — which didn’t exist on our site before — especially for this so that we could string a bunch of quotes together and still be readable.
Using Overview was pretty easy, especially since it has a web interface now that you can upload your documents to. You have to remove any commas, first, however. We augmented some clusters with simple keyword searches of thematically related words that the algorithm might not be able to connect the dots between. For instance, if military was one cluster, we might want to look at Navy and Army as related words. This might not actually give any extra insight since the algorithm is looking for clusters anyway so it might pick up on those. But it could help guide a more analog approach once the machine has broken things down into potentially interesting categories.
We clustered the spreadsheets of responses independently of one another, but one analysis would be to see if the machine would divide them automatically into two camps. Always more analysis to be done than deadlines allow. Here’s the anonymized data though, let us know at NewsBeastLabs@gmail.com if you find anything interesting.