Image via WikipediaContinuing to clear out my inbox, aka my brain, before I shut down for the year.
The other day I was listening to Technometria, with Eric Norlin as the special guest. The episode, about The Implicit Web touched upon a lot of interesting topics. Among other things they talked about Stocktwits, which I’ve been meaning to look into for a while, just cause some of the people using it are smart folks, but I wasn’t quite sure how it worked.
The service positions itself as a “social, stock microblogging service”, which uses Twitter as a content production platform, and then adds on to it a stock-centric structure. This immediately appealed to me, since it’s an excellent example of the data finds the data, then people find the people. I believe the way it works is around stock symbols, being able to track them via the Twitter API. What especially made me think about this were Pierre’s blog post and tweets about using Twitter for bioinformatics. This fits into some general paradigms as well, those around harnessing information streams.
One reason I don’t necessary buy into dedicated scientific social networks is that for many tasks, existing frameworks, e.g. Twitter, FriendFeed, LinkedIn, etc do a pretty good job of harnessing collective intelligence since we already have such rich information streams. Other communities have done a pretty good job of leveraging these platforms, perhaps we’ll start seeing the science types do so as well.
Web as platform: Twittering for stocks and proteins
The other day I was listening to Technometria, with Eric Norlin as the special guest. The episode, about The Implicit Web touched upon a lot of interesting topics. Among other things they talked about Stocktwits, which I’ve been meaning to look into for a while, just cause some of the people using it are smart folks, but I wasn’t quite sure how it worked.
The service positions itself as a “social, stock microblogging service”, which uses Twitter as a content production platform, and then adds on to it a stock-centric structure. This immediately appealed to me, since it’s an excellent example of the data finds the data, then people find the people. I believe the way it works is around stock symbols, being able to track them via the Twitter API. What especially made me think about this were Pierre’s blog post and tweets about using Twitter for bioinformatics. This fits into some general paradigms as well, those around harnessing information streams.
One reason I don’t necessary buy into dedicated scientific social networks is that for many tasks, existing frameworks, e.g. Twitter, FriendFeed, LinkedIn, etc do a pretty good job of harnessing collective intelligence since we already have such rich information streams. Other communities have done a pretty good job of leveraging these platforms, perhaps we’ll start seeing the science types do so as well.