Check out (and like, tweet and +1!) the civilometer prototype site

In the spirit of "show > tell", I spent a good chunk of last week building a prototype site for my civilometer proposal for the Knight Foundation's news challenge.  Please check it out and support us on twitter, facebook and google!  Tweets with the #newschallenge hashtag would be particularly appreciated.


Here's the link: www.civilometer.com

Here's a screenshot:

A screenshot, for your viewing enjoyment.

For those of you joining the story late, the proposed project is a public-facing site for political civility.  The site is designed as a data playground to hold politicians and newsmakers accountable for what they say.  We would take in real-time media feeds, and apply scientific civility-measuring techniques from my dissertation.  A suite of data visualization tools would enable users to ask data-driven questions about civility, and create and share cool graphs of their findings.

There's *a lot* that you could do with all this data.  My hope is that by building a public site (rather than hiding our findings in obscure academic journals) we can inject a bit more accountability into public discourse.  I'm really excited about the chance to build something genuinely productive with the research I've been doing the last five years of my life.

To make all this happen, I've applied for a grant from the Knight foundation.  Part of their judging criteria is public support.  Judging is happening right now.  (*bites nails in trepidation*).  If you like this idea, please head over to the site (www.civilometer.com), and tweet, like, and share the idea with everyone you know.

Thanks!



Warning: the site looks best in recent versions of Firefox and Chrome.  I haven't really tested it on IE, or Safari.  It looks decent on my kindle, though!  If we get funded, I'll make sure it looks good for all you you poor corporate Microsoft slaves as well.

Announcing QuoteWars2012!

Just in time for the Florida primary, my brother and I have released a site about the 2012 elections: QuoteWars2012.

The site lets you quiz yourself on quotes by Obama, Romney, Gingrich, and other presidential hopefuls.  Think of it as a gamified survey: like a survey, it collects data about public opinion, but it's also designed to be fun and informative.

The site in public beta -- fun and usable, with a few minor bugs.  We're looking for feedback on how to expand and improve the site.  Please play the game, forward far and wide, and let us know what you think!

More about political microtargeting as data science

Two interesting articles about how campaiging is being influenced by data science.

From Slate: Anatomy of a narrow victory: Romney's Iowa win took a lot more than money

And from BusinessWeek (HT Dad): OBAMA Campaign’s Secret Weapon: Geeks.

(Photo from BW.  Love the github ref.)

A few observations on data versus money, data-driven decision making, and whether we really want more data in politics...

Data set: pretty much every candidate in any of the US primary elections (house, senate, governor, president)

Working on a project on social media and U.S. primary elections, I couldn't find a good, machine-readable listing of candidates.  (Project VoteSmart doesn't do primaries.)  So I crowd-sourced it on mturk.  Here's the data, in json format (documented below).

For each election, we asked for as many candidate names, parties, and campaign websites as turkers could find.  We also asked for websites to verify the information, with the stern warning, "To receive credit for your work, you must include stable URLs to credible sites where you found your information."

Here's a screenshot of the mturk task:

Some details:

We vetted the data by running each task twice and comparing responses.  Wherever we found discrepancies (there weren't very many), we fixed mispellings, checked to make sure candidates were real, etc.  Overall, it worked pretty well.  We found nearly 2,000 candidates across the almost-500 elections.  I'm guessing the final data contain a handful of mistakes, but not many.  It is, as they say, good enough for government work.

Here's the data format.  The main file is an array of election objects:

election :
    id : a unique ID for the election
    office : "president" / "senate" / "house" / "governor"

    state : The name of the state where the election is being held.  N/A for president.
    district : The congressional district where the election is being held.  N/A for everything except house races.
    candidates : an array of candidate objects.

candidate :
    name : the candidate name
    party : The candidate's party (Republican / Democrat / Other)
    websites : an array of website URLs.

I'm putting this out in case anybody else was searching for the same kind of data. I'd love the hear about any mistakes and/or useful applications for it.  Cheers!

Authorship of Ron Paul newsletters -- statistical text analysis and Bayesianism

In the midst of all the hullabaloo about Ron Paul's decades-old racists newsletters, and his denial that he "never read that stuff," I ran across an interesting attempt to adding some data to the discussion.  Filed under "pointless data exercises" and "politics," blogger Peter Larson has used text analysis to compare his blog, Ron Paul's recent speeches, and the original newsletters.  He calls his results a smoking gun, with a question mark tacked on, and argues that Ron Paul wrote most of the original newsletters.

Before I go on, let me say that this is a really cool application.  Instead of the he-said-she-said debate that's running in the media, this piece brings some actual data to bear on the conversation.  Bravo!

That said, now I'm going to harp on statistics and inference.  The problem with Larson's analysis is that he never addresses the question, "If Ron Paul didn't write the newsletters, who did?"  Without answering that question, and putting some probabilities behind it, it's going to be very hard for text analysis to prove the issue one way or another. (Larson admits this on his blog.) Right now, his analysis proves that between himself and Ron Paul, Paul is much more likely to have written the letters.  Not exactly a smoking gun.


That said, it's interesting data.  For the record, I'm mostly convinced.  In my mind, the statistics are flawed, but they still lend some weight against Paul.  Oddly enough, Larson's finding that several of the letters were probably *not* written by Ron Paul was particularly persuasive.  It feels human and messy, the way I would expect this kind of thing to be.



Final thoughts, mainly intended for my statistically minded friends: yes, this is an unabashedly Bayesian perspective.  I'm demanding priors, which probably can't be specified to anyone's satisfaction.

IMHO, the frequentist approach has even deeper problems.  From a frequentist perspective (which is where Larson's original, and deeply confusing p-values come from.) we use Paul's recent speeches and writings to estimate some parameters of his current text-generating process.  We then compare the newsletters to that process and estimate the probability that the older text was generated by the same process.

Problem: we *know* the old text was not generated by the same process.  It was written (allegedly) by a younger Ron Paul, on different topics, speaking into a different political climate.  Without a broader framework, it's impossible to determine whether the differences are important.  The Bayesian approach provides a direct way of assessing that framework.  The frequentist approach doesn't -- at least not that I can see, without jumping through a lot of hoops -- and in the meantime, it obscures the test that's actually being conducted.