NewsBeast Labs

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.

-Michael

02/12/13

UPDATE: FEB 10 @RepsGunTweets has been changed to @YourRepsOnGuns. Check out www.ThisIsYourRepOnGuns.com for the ongoing project.

Brian Abelson is a data scientist who is graciously donating his time at NewsBeast Labs before he starts a full-time position as a Knight-Mozilla Open News Fellow at the New York Times in February.

For an upcoming project on the gun debate, we’ve been monitoring statements representatives have made on the topic. As President Obama prepared to unveil his proposal for gun control on Wednesday, Michael and I were curious to see the reactions of representatives to the highly publicized announcement and be able to report that in real-time. Given the degree to which breaking news is now reported (and responded to) on social media, we thought it would be useful to build a bot to log officials’ comments on certain issues and present them in real time. Such a tool could be used by news rooms to engage their readers on a continuous basis by aggregating and serving content from members of particular communities or who serve on different committees.

@RepsGunTweets was born.

We were inspired by the work of 2013 Mozilla-Knight OpenNews fellows who recently built a prototpe for an app called “if (this) then news,” a news-oriented take on IFTTT – a site for linking triggers from gmail, twitter, dropbox, and other services to actions on the web. Applying this logic to news coverage, the fellows created the shell for a tool that would monitor live data streams, detect important events, and issue notifications. As Vice President Biden took the mic, we started furiously coding up a bot that would follow the twitter accounts of US Representatives and retweet any comment that included “gun”, “assault weapon”, “firearm”, or other relvant keywords. After a couple hours of missteps and headaches, we eventually got @RepsGunTweets up and running. In the last ten days, the bot has logged 307 tweets; two-thirds of which came in the first three days. We’re still analyzing the conversation but one interesting observation is representatives who are not in favor of gun control tend to link to longer explanations of their position on their website instead of tweet a comment.

Under the hood

At its core a retweet bot is a pretty simple tool: Follow a feed, find what matters, and serve it back up under a single account. The harder part is figuring out how to accurately communicate with Twitter’s API. Using tweepy for python we were able to easily access twitter’s numerous methods. All we needed to provide it with were the the consumer key, consumer secret, access token, and access token secret for an application generated on http://dev.twitter.com/apps. The bot follows CSPAN’s member of congress list and applies a regular expression for the desired keywords and retweets any matches.For even more technical info, check out this Github page

01/24/13

Six Months in Review

NewsBeast Labs is roughly six months old and we’ve had a lot of fun. This tumblr has most of our projects for the past few months but there are a bunch from before our launch. Here’s a rough list of projects we’ve done so far.

Legal Experts Decode the Supreme Court’s Obamacare Ruling - Our very first project! We launched it the day we got DocumentCloud, which was also the morning of the Supreme Court ruling on Obamacare. We asked two law professors to make margin notes in the text of the ruling as they were reading it for the first time. Readers could follow along and read experts’ reactions as the conversation was happening.

Digital 100: Who’s Following Whom? - A network visualization of how Newsweek’s list of influential people in the digital space interact with each other on Twitter.

Obamacare: It’s Cheaper! - I like to call these “Story Visualizations” - visual presentations of stories that could run as a list or as text, but are much more interesting visually. Matt DeLuca and I did a side-by-side on how Obamacare would affect different age groups’ healthcare spending.

2012 Olympics: The Latest Medal Tally - We had a live-updating Olympic Medal Count, (with a snazzy sortable table that I’ve written about before) that I worked on with our awesome intern Sarah Hedgecock. We also did a version of it for our right rail (sidebar).

Interactive Map: London’s Olympic Transformation - The Olympic Park rose from the rust. Sarah and I also did a satellite view before and after interactive that included a bunch of info on the star-chitect buildings.

Interactive Map: The U.S. Shooting Epidemic - Following the Aurora shooting, Brian Abelson and I made an interactive map of multiple-victim shootings since 2005 and asked readers to respond with their memories. We published a selection of the reader responses here. The full spreadsheet list is here.

As Income Inequality Widens, Rich Presidential Candidates Dominate - Lauren Streib and I worked on a chart (she did all the numbers), showing presidential income over the years. I remember this one chart taking four hours from start to finish for some reason…

Big Guns Inside the National Rifle Association Leadership - Who’s leading the NRA? I worked on a project with three colleagues Caitlin Dickson, Eliza Shapiro and Kevin Fallon on the NRA’s leadership. They dug through 990 forms and put together small profiles of the people at the top. We put it together in mosaic-style presentation. Normally this type of story would be a gallery format but since it’s not picture-based, we decided to create something more conducive to reading a lot of text.

SuperPAC App Election Ad Interactive - We partnered with the Super PAC App, an iPhone app that would identify political advertisements on TV and give you information about that group, such as how much money it was spending this election and articles about them. We made a web interface to their data to provide readers with more context for outside spending groups.

Interactive Map: Who’s Protesting Where? - When the Middle East erupted in protests in response to an anti-Muslim video uploaded to YouTube, Eliza Shapiro and I put together a visual guide with information on each protest as well as contextual information on each country. It was an interesting map to built since we had both point and polygon layers to deal with for hover states. As with all of our interactive maps, we used CartoDB.

Obama and Romney’s Bundlers - If bundlers had baseball cards, this is what they’d look like.We took a look at the biggest bundlers for each candidate. Collect ‘em all.

The Rise of the Political Non-Profit - How so-called “Dark Money” was influencing the 2012 election was one of the themes in a three-part series John Avlon and I wrote called the Super PAC Economy. This animated timeline overlays non-profit political expenditures and significant court decisions (Citizens United and lesser-known decisions) that determined what role these groups could play in politics.

The Dark Money Shuffle - Also in that series, we worked with Robert Maguire of the Center for Responsive Politics who had been compiling a database of grants that non-profits gave to each other. For the first time, we diagrammed this opaque world of money transfers that is only visible by manually going through hundreds of IRS forms. Full article.

Election Right Rail - Showing the latest polls from battleground states, how those states voted historically, median income, and latest unemployment figures, our politics sidebar was full of context. It no longer lives anywhere on our site but you can see a standalone version how it looked on the eve of the election through the linked title.

Note: We did all of these projects before starting this tumblr. You’ll find write-ups for the projects that follow but if you want to know how we built any of the stuff above, send me a message at @mhkeller.

Debate Dashboard and Bingo - Brian, Sam, Vitaly Korenkov (one of our awesome developers) conceived of a great debate night dashboard. We had a livestream, a live chat with our commentators and a poll from Urtak, which is a polling platform that lets you pose simple yes/no/maybe questions to readers. It also lets readers submit questions they want other people to answer so it’s a good back and forth between questions we’re interested in and what our audience is interested in. We’re often into giving our readers a voice on the site so we liked it a lot. I came in during the last few hours before we were going to go live (a.ka. after all the hard work was done) and added a bingo card. The coolest part about it is the Bingo validation. The card checks how many you have in a row vertically, horizontally, and diagonally and tells you how many you need to win. NewsBeast Labs post.

Ground game: Obama Campaign Opens Up Big Lead in Field Offices - The airwave battle was being covered left and right, but we wanted to know what was happening on the ground. We scraped the two campaigns’ websites to map out their local HQs nationwide and found a big discrepancy between the two camps. In Ohio, for instance, Obama had a presence in so many counties where Romney didn’t that 10 percent of the state’s population lived in a county where the only volunteer center was an Obama HQ.

Technical note: We used CartoDB again for this map and it was a huge help. In the accompanying article, we ran interactive maps of Florida, Ohio, and Virginia. These separate maps required no real extra programming or map making since CartoDB builds your map by querying a database. By setting our map query to ‘SELECT * from national_map WHERE state = FL’ we had a local map in minutes that we could swap out for another state if needed, which indeed ended up happening. NewsBeast Labs post.

Interactive Hate: The Great Obama-Loathing Canon - Matt DeLuca and I teamed up again to solve the perennial problem of how do you present a lot of information to the reader in a way they can digest in bites that make sense. This time, we presented over a hundred anti-Obama books in a mosaic that you can filter down to different subject matters. NewsBeast Labs Post.

HavingTroubleVoting.com - We did an experiment on election day asking our readers, or anyone really, if they were having trouble voting, and if so, what kind of trouble. We plotted the responses on a map below and color-coded the markers based on the type of problem. We partnered with Mother Jones on it to help us go through the responses to find patterns and to contact people to tell their story. Our own reporters used the database in stories about massive lines and machine malfunctions. We’re totally honored and floored when CJR named it No. 2 in their Must-Read Interactives of 2012! More about it in our NewsBeast Labs post.

Election Night Interactive Map and Dashboard - A lot of teamwork went into our election night coverage from the development team, social, design… the list goes on. We took over our home page on election night with video commentary, a live updating tally, a live chat, article updates and more things that you could probably put a “live” prefix in front of. The map lives on in the linked title, a screenshot lives in our NewsBeast Labs post about it.

‘It Was Like a War Zone’: Hurricane-Ravaged Staten Island Reels - In the wake of the trauma caused by Hurricane Sandy, we did a map of Staten Island victims. It shows how many of the fatal tragedies were concentrated on the east side of the island.

Not-So-Super PACs: 2012’s Winners and Losers - DeLuca and I teamed up again to produce this tally of who made good investments this election cycle. There’s a long post about it, including some failed versions in our NewsBeast Labs post.

Interactive Holiday Gift Guide - Lizzie Crocker, Isabel Wilkinson and I help you find out what sub-culture your friends might belong to in this gift guide flow chart.

Own a Gun? Tell Us Why? - December brought another terrible shooting and has caused much thought over the state of gun laws. We wanted to hear from rational people on both sides of the debate by lettings readers complete the sentences, “I own a gun because…” or “I don’t own a gun because…”. In three days, we had over 1,300 responses that represented very civil remarks from each group, for the most part. We analyzed the responses and did a state-by-state breakdown of the common themes. We used some interesting algorithmic clustering to find these patterns so expect a write-up soon. For now, read the post on how the project was born and how we collected the responses.

12/28/12