Posts tagged "the daily beast"
It was the Monday morning news meeting and all we could talk about was Dennis Rodman, Kim Jong-un, and Vice Media. It was the strangest story of the week, and utterly riveting. But there was more to it than what had already been reported.
The Vice show is funded by HBO, which is owned by Time Warner, which has a boatload of shareholders. Vice Media itself has a whole range of wealthy investors, including former Viacom CEO Tom Freston and The Raine Group, a who’s who of one-percenters. Meaning: a lot of people stand to benefit from the hospitality/propaganda machine of the one of the world’s most notorious dictators.
Reporter Caitlin Dickson started looking at the various connections, using LittleSis.org, an online database that tracks the social connections among the powerful—politicians, business leaders, lobbyists, hedge funders, etc. LittleSis is a project of the nonprofit Public Accountability Initiative and a great, easily-searchable, user-friendly source for reporters. You can search people and companies, find out how they’re connected to each other and to whom they donate money.
Check out the screenshot for an example, and the story for the final product. Caitlin was on deadline pressure so was only able to scrape the surface on the Vice story, but there’s no doubt much more to mine.

-Paula Szuchman, deputy managing editor

It was the Monday morning news meeting and all we could talk about was Dennis Rodman, Kim Jong-un, and Vice Media. It was the strangest story of the week, and utterly riveting. But there was more to it than what had already been reported.

The Vice show is funded by HBO, which is owned by Time Warner, which has a boatload of shareholders. Vice Media itself has a whole range of wealthy investors, including former Viacom CEO Tom Freston and The Raine Group, a who’s who of one-percenters. Meaning: a lot of people stand to benefit from the hospitality/propaganda machine of the one of the world’s most notorious dictators.

Reporter Caitlin Dickson started looking at the various connections, using LittleSis.org, an online database that tracks the social connections among the powerful—politicians, business leaders, lobbyists, hedge funders, etc. LittleSis is a project of the nonprofit Public Accountability Initiative and a great, easily-searchable, user-friendly source for reporters. You can search people and companies, find out how they’re connected to each other and to whom they donate money.

Check out the screenshot for an example, and the story for the final product. Caitlin was on deadline pressure so was only able to scrape the surface on the Vice story, but there’s no doubt much more to mine.

-Paula Szuchman, deputy managing editor

After the Newtown shooting in December, we had a meeting over the phone to discuss our coverage. We decided to have a two speed approach: a quick reader-driven story about why they do or don’t own guns (which we’ve written about a bit on this blog), and a deeper-dive look at the anticipated legislative issue that this and other recent shootings seemed to be bringing about, which we launched Monday as www.ThisIsYourRepOnGuns.com. The project idea grew out of the simple problem that not many people can name their representatives off the top of their head, let alone know their exact stance on gun control or how to get in touch to make their voice heard.
Eliza Shapiro, Abby Haglage and Caitlin Dickson did some awesome reporting for all 530+ representatives, digging through their voting records and previous public statements to distill their position to one of four categories: Opposes reform, Supports reform, Swing vote, or Unclear. We kept track of the sources, too, so that we could present representatives’ statements to the reader when the final thing was done. 
Brian Abelson was also around to rig together @RepsGunTweets (since renamed @YourRepsOnGuns), which served as both a tool to monitor reps’ statements to see what category they fell into, as well as an open feed for anyone interested in the topic to follow on Twitter. Read about how that was built in this blog post.
The interactive currently stacks up the number of reps in each category and lets you do a combination filter by different criteria such as chamber, party and state. You can see things like how likely legislation is to pass each chamber and where different states stand. Importantly, too, you can put in your address read information on your House representative and two Senators. Using information compiled by the Sunlight Foundation, it gives you their phone, fax (for those that prefer the fax), address, twitter, website and Facebook page so you can get in touch with them. We also pulled in each representatives NRA grade and their rating from the Brady Campaign to Prevent Gun Violence to give more context to their legislative history.
My favorite part of it though, is that we’ll be updating it as the gun debate goes on. We’ve already received emails from readers who have contacted their reps with statements that we’ll add and one person sent us a local news story from their congressperson that will move him from the Oppose reform to a Swing vote. We’ll mark these updates on the landing page so people can follow along and readers can leave their email to be notified of updates.
We also did this as its own URL similar to how we did www.HavingTroubleVoting.com. As a resource and tool that was going to hopefully have a long life, we felt an easy to remember and dedicated page showed our readers that this was something they could keep coming back to.
Under the hood
The hardest part of this was getting all of the data from multiple different sources into one nice database. We had a few different people researching, different numbers coming in from different places, and multiple editors editing. We used Google Spreadsheets and good spreadsheet etiquette to make sure people were marking the categories the same way and joined them in R. 
To make the stance information simple to update, the map copies that information from the main table on load instead of storing it separately with the map data.
The main page uses Isotope.js, which we’ve used a bunch before. But this was a little tricky because we needed to sort them into four columns. Fortunately, there’s some crazy extension for Isotope that lets you do just that. The harder part was figuring out how to get it to display top to bottom instead of bottom to top. But buried in the “Tests” documentation was a page on how to make your elements stack right-to-left for languages like Hebrew and Arabic. It includes the settings to rotate the positioning, which worked.
The only fancy mapping feature is if you click on a district, the map automatically pans and zooms to fit the founds of that district. This is done using the ST_Envelope() function in PostGIS through CartoDB. ST_Envelope() returns the bounding box of a given feature which you can sent to Leaflet.js’s fitBounds() method to pan and zoom to that box. The only problem to be aware of is ST_Envelope() will give you an array of x and y values but fitBounds() is expecting the format to be in y then x (lat, then long). As long as you reorder the elements in your coordinate array, Leaflet will be happy.
Getting the aesthetics of the map right was a little tricky. I wanted to make sure that a highlighted feature’s outline appears above the other features but below its own fill so you get a bright white border and then a subtler inner border. If you follow the symbol drawing order and compositing option rules in CartoCSS it becomes manageable.
From the failures folder
Here’s what the original mock-up looked like, which we weren’t too far off from. I reworked the top nav hierarchy into two main buttons, added more color and turned the rep detail elements into three columns instead of rows so it was more compact and graphic.

-Michael

After the Newtown shooting in December, we had a meeting over the phone to discuss our coverage. We decided to have a two speed approach: a quick reader-driven story about why they do or don’t own guns (which we’ve written about a bit on this blog), and a deeper-dive look at the anticipated legislative issue that this and other recent shootings seemed to be bringing about, which we launched Monday as www.ThisIsYourRepOnGuns.com. The project idea grew out of the simple problem that not many people can name their representatives off the top of their head, let alone know their exact stance on gun control or how to get in touch to make their voice heard.

Eliza Shapiro, Abby Haglage and Caitlin Dickson did some awesome reporting for all 530+ representatives, digging through their voting records and previous public statements to distill their position to one of four categories: Opposes reform, Supports reform, Swing vote, or Unclear. We kept track of the sources, too, so that we could present representatives’ statements to the reader when the final thing was done. 

Brian Abelson was also around to rig together @RepsGunTweets (since renamed @YourRepsOnGuns), which served as both a tool to monitor reps’ statements to see what category they fell into, as well as an open feed for anyone interested in the topic to follow on Twitter. Read about how that was built in this blog post.

The interactive currently stacks up the number of reps in each category and lets you do a combination filter by different criteria such as chamber, party and state. You can see things like how likely legislation is to pass each chamber and where different states stand. Importantly, too, you can put in your address read information on your House representative and two Senators. Using information compiled by the Sunlight Foundation, it gives you their phone, fax (for those that prefer the fax), address, twitter, website and Facebook page so you can get in touch with them. We also pulled in each representatives NRA grade and their rating from the Brady Campaign to Prevent Gun Violence to give more context to their legislative history.

My favorite part of it though, is that we’ll be updating it as the gun debate goes on. We’ve already received emails from readers who have contacted their reps with statements that we’ll add and one person sent us a local news story from their congressperson that will move him from the Oppose reform to a Swing vote. We’ll mark these updates on the landing page so people can follow along and readers can leave their email to be notified of updates.

We also did this as its own URL similar to how we did www.HavingTroubleVoting.com. As a resource and tool that was going to hopefully have a long life, we felt an easy to remember and dedicated page showed our readers that this was something they could keep coming back to.

Under the hood

The hardest part of this was getting all of the data from multiple different sources into one nice database. We had a few different people researching, different numbers coming in from different places, and multiple editors editing. We used Google Spreadsheets and good spreadsheet etiquette to make sure people were marking the categories the same way and joined them in R. 

To make the stance information simple to update, the map copies that information from the main table on load instead of storing it separately with the map data.

The main page uses Isotope.js, which we’ve used a bunch before. But this was a little tricky because we needed to sort them into four columns. Fortunately, there’s some crazy extension for Isotope that lets you do just that. The harder part was figuring out how to get it to display top to bottom instead of bottom to top. But buried in the “Tests” documentation was a page on how to make your elements stack right-to-left for languages like Hebrew and Arabic. It includes the settings to rotate the positioning, which worked.

The only fancy mapping feature is if you click on a district, the map automatically pans and zooms to fit the founds of that district. This is done using the ST_Envelope() function in PostGIS through CartoDB. ST_Envelope() returns the bounding box of a given feature which you can sent to Leaflet.js’s fitBounds() method to pan and zoom to that box. The only problem to be aware of is ST_Envelope() will give you an array of x and y values but fitBounds() is expecting the format to be in y then x (lat, then long). As long as you reorder the elements in your coordinate array, Leaflet will be happy.

Getting the aesthetics of the map right was a little tricky. I wanted to make sure that a highlighted feature’s outline appears above the other features but below its own fill so you get a bright white border and then a subtler inner border. If you follow the symbol drawing order and compositing option rules in CartoCSS it becomes manageable.

From the failures folder

Here’s what the original mock-up looked like, which we weren’t too far off from. I reworked the top nav hierarchy into two main buttons, added more color and turned the rep detail elements into three columns instead of rows so it was more compact and graphic.

-Michael

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

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

Oh hey! Adweek’s Charlie Warzel wrote a story about our team!

Oh hey! Adweek’s Charlie Warzel wrote a story about our team!

Notes and images from an ever-growing digital newsroom.

Newsweek & The Daily Beast

Contributors:
Brian Ries & Sam Schlinkert

Formerly:
Michael Keller, Andrew Sprouse, Lynn Maharas, & Clarisa Diaz

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