

Show HN: Tracking voter discussion of the Presidential debates - jdunck
http://live.votizen.com/
Votizen is working to remove the influence of money in politics by making political decision-making more peer-to-peer.<p>We're using Twitter and our API to pull tweets from only registered voters, and showing how the discussion breaks down among the parties. This has never been done before as far as I know, not even by the major media outlets.<p>Please check it out and let me know what you think.  If people are interested, we'll work to improve it for the further debates and campaigns.
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jaysonelliot
It's a fun visualization to have up as a dashboard, I think I'll keep it
running during the debates.

I love the design; it's very easy to read, and the simplicity and color make
it easy to digest information at a glance.

My constructive criticism:

For the word frequency charts at the top of the screen, I'd weed out common
words, and/or try to combine words into meaningful phrases.
<http://www.wordle.net/> does a good job with this (weeding out common words,
not discerning phrases).

The number one thing I think people would be interested in would be some kind
of sentiment analysis. Perhaps a knowledgable HN'er could suggest a service
you could use for this?

From a UX perspective, I'd like the ability to pause and rewind the twitter
streams, or filter them for specific words, phrases, geographic locations, or
demographics.

One final nitpick, I would suggest using image meta-tags for sharing on
Facebook. I posted the live.votizen.com page to my timeline, and there was no
image to accompany the link. Here are the meta-tags you can use for richer
sharing: <http://davidwalsh.name/facebook-meta-tags>

But yes, overall, I love this.

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jdunck
We have weeded out a bunch of stuff and are actually handling n-grams, but
apparently not with enough weight since they aren't showing up ever.

What words do you think shouldn't be there?

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endtime
Looks, Look, Good

Also, you should think about stemming - "Looks" and "Look" should be in an
equivalence class.

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erikrose
Perhaps we can bang a Snowball stemmer against it and get pretty good coverage
on the whole language.

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Zenst
I'd be interested in the trending between points. For example I would just
love to see how the trend went after a candidate said certain things and
indeed trend based upon the last N minutes. The one which Romney says "I'm
gonna (sounded like that too me) stop the subsidy to PBS", Now that would be
interesting too me, along with many other statements by them both.

Fascinating stuff, I also noted a few posts positive to romney that wer
associated as obama as obama was mentioned and vice a versa. I suspect a first
mentioned corralation, though have seen exceptions to that, i'd paste examples
but no pause button and late. To truely seperate those type of posts is
something were you will need to crowdsource down the line if only to learn a
rule set. Though tweets are short so permutations will not be that dynamic as
apposed to more open social means and with that the limitation can only help.

Anybody aware of a ruleset that can weight one person over another based upon
the content of block of text that mentions both people. Certainly would have
uses beyond this. Amazing how something so simple as that involves alot more
than you would initialy think.

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erikrose
What you're talking about in the last paragraph is sentiment analysis. This is
really cool stuff, but it's tricky. Even if you have 2 humans do sentiment
analysis independently, they agree with each other only 70% of the time.
Computers tend to do worse, of course.

What makes it extra challenging is that tweets are short, so you have wide
error bars on the sorts of math you'd depend on in larger corpuses. I think
you'd have to actually bang a parser against it and try to understand what the
tweeter is saying.

~~~
erikrose
Btw, here's an example of somebody trying to do sentiment analysis on tweets:
[http://www.sentiment140.com/search?hl=en&query=romney](http://www.sentiment140.com/search?hl=en&query=romney).

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Zenst
Thank you, sentiment analysis - I just love that term.

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engtech
As a non-US guy, this is pretty cool.

It's working really well for live updating.

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jaysonelliot
How are you matching Twitter accounts to voter registration?

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jdunck
We have voter rolls (preprocessed) into elastic search. We do search using
data available from twitter (name & location, mostly). We can't match all
twitter users who are voters, but we have a very low false-positive rate.

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erikrose
And getting lower tomorrow. And then going up in efficacy, while maintaining
confidence. There's a ton more we can do with this. :-)

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jdunck
What do you like? What don't you like?

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michael_fine
I love the whole concept and execution, except I think the tweets should
scroll by slightly slower, as sometimes you don't have time to read a tweet
before it disappears.

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jdunck
Yeah, it's a tough call. If we slow it down, we'd have to show fewer as the
underlying stream is zipping by. We considered pause or pause-on-hover, but
sort of assume people will glance at it, not stare at it, if that makes sense.
Gestalt, not reading?

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witoldc
Interesting. I'm particularly interested in how you got this matched up
against voter lists. Some states are quite a PIA to collect.

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erikrose
You're telling me. :-) We spent 2 years getting all those lists together and
normalizing them. We're midway through a refresh now—new normalization
framework, new matching algorithms, etc., etc.

