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Datafest: Hacking Campaign Finance Data over a Weekend

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This weekend I had the pleasure of helping to run Datafest, a two day campaign finance hackathon at Stanford. The event brought together around 60 journalists, technologists and students, many of whom had no prior experience in campaign finance or data driven journalism. The enthusiasm and dedication of the group--as well as the results--were truly heartening to see.

On Saturday morning the participants split themselves into ten teams, each of which came up with an original project to implement over the following day and a half. Some teams focused on original reporting using political influence datasets. Many produced visualizations. On the more technical end, there were interactive apps and statistical modeling. At the end of the weekend we announced an overall winner and three runners up, all of whom deserve a bit of recognition for what they achieved in such a short time.

Team Frienemies Screenshot

Team Frienemies took home first place with a system for grouping and visualizing political entities by their common donors. The project is compelling not so much for its catchy visualizations, but for the insights they were able to derive. Groups that are non-partisan in name only, such as Emily's List or Club for Growth, are correctly placed at the center of the Democratic and Republic clusters. The visualization also suggests non-obvious associations, such as various telecommunications industry groups leaning Democratic. The project authors also proposed using the tool as a recommendation system: donors could find other candidates or organizations they may support and political groups could find other funders that may be interested.

Team Gophers chose to dive deep into the influences of one company as a case study in using the many datasets available. Their visualizations touched on campaign finance, the revolving door of lobbyists, federal contracts, and bills before Congress. One key insight they were able to show was that the company gave predominantly based on committee membership of the member, not based on ideology, expected election outcome or geography.

Team KeyStoners took a slightly different approach: they focued on one particular issue--the Keystone Pipeline authorization in Congress--and looked at a variety of influences on the outcome of the vote. In particular, they tried to explain what accounted for 41 Representatives who flipped their position before the final vote on the bill. Interestingly, the found that rather than campaign contributions, lobbying or prior voting record, by far the most explanatory factor was being a Democrat in a district surrounded by Republican districts.

Team MostExcellent started with a simple but interesting question: is the out-of-state money going into the Wisconsin Governor's race unusual? Through data analysis and interactive visualizations they show that out-of-state money is not an anomaly. This was combined with two short news pieces on the topic, giving a nice example of using data both to investigate a story and to present that story to the reader.

If you're interested in hacking on your own, check out the data section of Influence Explorer, where many of the teams at Datafest found their data. And many thanks to Knight Fellow Teresa Bouza for organizing the event!

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In Depth with Campaign Finance Data

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Influence Explorer and TransparencyData are the Sunlight Foundation’s two main sources for data on money and influence in politics. Both sites are warehouses for a variety of datasets, including campaign finance, lobbying, earmarks, federal spending and various other corporate accountability datasets. The underlying data is the same for both sites, but the presentation is very different. Influence Explorer takes the most important or prominent entities in the data--such as state and federal politicians, well-known individuals, and large companies and organizations--and gives each its own page with easy to understand charts and graphs. TransparencyData, on the other hand, gives searchable access to the raw records that make up each Influence Explorer page. Influence Explorer can answer questions like, “who was the top donor to Obama’s presidential campaign?” TransparencyData lets you dig down into the details of every single donation to that campaign.

Every chart and figure in Influence Explorer is derived from the detailed records in TransparencyData. But correctly computing totals from the raw records isn’t always straightforward. This article will explain how the figures on Influence Explorer are computed from TransparencyData records, and along the way show some of the intricacies of campaign finance rules and limitations of the data.

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Corporate Accountability Data in Influence Explorer

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Sunlight reporter Ryan Sibley published a short post today on various efforts of Koch Industries to influence environmental policy. That piece is a perfect example of the value of bringing disparate datasets together under one site. Ryan pulls in data on Koch Industries’ lobbying activities, EPA enforcement actions against the company and a Koch executive that sat on an EPA advisory committee–all from Influence Explorer and TransparencyData.

The story is based on some of the new data sets that we’ve added to Influence Explorer: EPA enforcement actions, public comments on federal rulemakings and corporate employees on federal advisory committees. I’ll give a brief overview of each.

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Tuning Up Influence Explorer

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For the past year we’ve been busy adding data and features to Influence Explorer, our central source for data on money and influence in politics. From our original three data sources (federal campaign finance, state campaign finance and federal lobbying) we’ve now expanded the site to include earmarks, federal spending, contractor misconduct and EPA enforcement data. We’ve also released tools such as Checking Influence and Inbox Influence that put political influence data in a context that’s relevant to users.

After tackling these larger projects we decided to step back and revisit a few things that we didn’t get perfect the first time around. In a number of places we’ve tweaked our methodology, cleaned up our data and added more context. The result should be more accurate and useful information across the board.

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Influence Explorer Text API: Adding Influence Data to Text

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Last month I wrote about Inbox Influence, a plugin for Gmail that highlights the political influence of people, companies and politicians mentioned in your emails. Gmail is just one example of a context where it makes sense to attach political influence data to text. (Another is news stories--see Poligraft.) But what about Yahoo Mail and Hotmail? What about Outlook and Thunderbird? What about RSS readers, PDF viewers and word processors? What about integrating political influence data into server-side systems such as blog engines and content management systems? Sunlight Labs can't do it all, which is why creating tools and APIs for developers is one of our primary missions. Indeed, nearly all of our products--including Inbox Influence--are powered by public APIs.

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Inbox Influence: Political Influence Data in Gmail

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Today we're officially launching Inbox Influence, the latest addition to our suite of political influence tools. Inbox Influence is a browser extension that adds political influence data to your Gmail messages. With Inbox Influence installed, you'll see information on the sender of each email, the company from which it's sent, and any politician, company, union or political action committee mentioned in the body of the email. The information is added unobtrusively and nearly instantaneously, and includes campaign contributions, fundraisers and lobbying activity. You can use it to add context to news alerts, political mailers and corporate emails, or just to see who your friends donated to in the last election. We hope that the tool will be of interest to journalists, activists and anyone interested in seeing the political activity of the people and organizations they communicate with.

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New On Influence Explorer: Fundraisers, Bills and More

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One of the primary goals of our site Influence Explorer is to show users a wide variety of influence data on one page. Today we're expanding the scope of influence data by integrating with three important Sunlight projects: Party Time, Lobbying Registration Tracker and OpenCongress. Along with integration with these sites we're also including new data from our partner organization, Center for Responsive Politics, that gives a far more accurate picture of the campaign donations of registered lobbyists.

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Influence Data APIs

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Followers of this blog are probably already aware of two of the main sites developed by our Data Commons team: TransparencyData.com and InfluenceExplorer.com. Both sites present a variety of influence related data sets, such as campaign finance, federal lobbying, earmarks and federal spending. Influence Explorer provides easy to use overview information about politicians, companies, industries and prominent individuals, while Transparency Data allows users to search and download detailed records from various influence data sets.

In this blog post I want to show how easy it can be to use the public APIs for both sites to integrate influence data into your own projects. I'll walk through a couple examples and show how to use both the RESTful API and the new Python wrapper.

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OSCON 2010

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Last week I was fortunate enough to attend OSCON in Portland, OR. This year OSCON hosted hundreds of talks on a dizzying array of subjects. The hot topics this year were definitely cloud computing and languages, established and emerging.

Most interesting to me though was the emphasis on government, social issues, and information freedom. Tim O'Reilly's opening keynote set the tone for many of the later talks by calling for the open source community to use its expertise in cooperative problem solving to address pressing issues in government and society. There were also keynotes from Portland's Mayor Sam Adams and DC's Chief Technology Officer Bryan Sivak on the importance of open source in local government.

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