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Hoosgot exploits the wisdom of the Blogosphere crowd

Hoosgot exploits the wisdom of the Blogosphere crowd

Tim Finin, 8:57am 2 January 2008

Technorati founder David Sifry launched a new service last week, when everyone was recovering from one holiday and preparing for another. Hoosgot (Who’s got …) let’s you ask the collective Blogosphere by posting a question on your blog or on the Twitter microblogging system. You need to include the term hoosgot in your blog post and @hoosgot in your twitter update to have it noticed.

Sifry explanation of how Hoosgot happened reinforces my belief that the greatest skill a practical computer scientist can have is being able to quickly test a new idea by turning it into running code.

You gotta love Holiday Weekends. Friday night (the 28th) The lazyweb popped back into my mind. I missed it. I started asking myself the question, “Why hasn’t anyone reconstituted the lazyweb?, What if we could rebuild the lazyweb for the 2008 web? What if we could take advantage of all the cool tools that have arrived in the last 5 years? Would it work?” Rather than wait around, I realized I could just build it, and maybe folks like me would use it. At about 5am on Saturday morning, the first prototype was up. I made some major changes, including twitter support Saturday night. And launch is today, on Sunday morning! Ain’t working on the web fun?:-)” (link)

Of course it helped that he could tweak Technorati to collect blog posts and tweets.

Will it work? Hooknows. One problem is spam, and Sifry is well positioned to deal with this. The other is that the wisdom of crowds is not uniform. Since your Hoosgot query is going out to a very broad group, a narrow question on an obscure aspect of Java programming will be a head scratcher to most. If you ask the blogmob for a movie recommendation, they will tell you to go see Norbit, which was 2007’s 29th highest grossing movie but also so unredeamably horrible that it almost killed Eddie Murphy’s career.

There are some possible things that could address these problems. Learning to spot Hoosgot spam and automatically adjust the model as it evolves is one. Another is to classify the Hoosgot queries by intent, topic and geography. Both of these are made more difficult if the queries are short, as they will be for Twitter-based queries. We’ve dealt with some of this in Akshay Java’s recent work on analyzing Twitter updates (
Why We Twitter: Understanding Microblogging Usage and Communities
).

(viaReadWriteWeb)


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