I promised myself that I wouldn't be drawn into blogging or tweeting about Twitter, but some of the comments flying around about #SXSW "information overload" highlighting the failure of Twitter to scale have irked me somewhat.
This is a basic problem in any information management system: you need to adapt how informaton is managed based on the volume and other characteristics of the data. And it's not necessarily something that can be automated, because information categorisation happens to be one of those things that humans are very good at.
If I ran a phpBB forum with a single forum for all posts, and saw a sudden increase in posts that made following the content difficult, I wouldn't run around screaming about the inability of forum software to scale. I'd just split the main forum into several sub-forums, based around the main topics.
If I had a single pile of papers on my desk and found it'd grown to a size where I could no longer find things quickly, rather than declaring that piles of paper were dead, I'd split the pile into several categorised smaller piles.
This is nothing new.
The issue seems to be that people have forgotten how the Twitter syntax has developed over time. @Replies (@<username>), retweets (RT<message>) and hashtags (#<topic>) were all conventions started by users, which virally became popular, and then started being supported by the various Twitter clients. This is a demonstration of the power of twitter: it's minialism means that people can use it in very different ways, and the community can extend and adapt it without needing to convince a central authority.
So calling for 'new analytical infrastructure', to me, is forgetting the essence of Twitter as a community driven medium.
So as a member of the wider Twitter community, what are my ideas for a solution? First you have to look at the problem a little closer. Hashtags are used in two ways: to allow your followers to easily access the wider context of your tweet, and to allow people following the wider context to find your contribution. The latter is a great information mining resource, and this is where the complaints around SXSW are stemming from.
I think the most obvious solution is similar to the examples above: either use multiple hastags to multi-categorise tweets, or use hierarchical hashtags to sub-categorise them. For a large event like SXSW, sub-tagging would allow much better filtering. For example, using #sxsw:social for social events.
Why would someone be motivated to use up more valuable tweet space with extended tags? For the same reason they use hashtags now: to improve the quality and usefulness of their stream to followers, and to surface their tweets to a wider community. Category overload is as much a problem for contributors who want to be found as it is for consumers looking for data.
If you happen to be following a large number of people who are attending something like SXSW, some enhanced Twitter tools might be nice. For example, being able to filter out tweets with a particular hashtag, or in-built group support and the ability to temporarily ignore updates from a group. But it is possible to handle now, either by using a more powerful Twitter client like TweetDeck, or by actively managing the people you follow.
Going forward, I think improved filtering is definitely going to be important when it comes to the usefulnesss of hashtagging, whether provied by Twitter or third parties. Right now though it seems silly to be saying that social media tools have failed to scale simply because people have dumped all of their content into a single category. There was a failure of users to anticipate and react to the volume of tweets, but nothing, I think, fundamentally flawed with the infrastructure itself.