On the Hype Machine’s Twitter Music Chart
We monitor Twitter for links pointing to tracks on the Hype Machine. We then give each of those tweets a number of points based on the number of followers (and the ratio of friends & followers) that person has. Finally, we add up all the points and figure out which tracks are tweeted by either the most influential twitter users, or by the largest group. Simple.
I like it because it’s simple, and because it offers a new view on the relative popularity of the music that appears on hypem.com; maybe all those twittered links to Hype Machine tracks are of no particular significance, but it’s well worth a little digging to see whether there’s anything there in that data. And I particularly like how open Anthony and team are about how the charts are built: there’s no secrecy about how and what they’re measuring.
What I like less is what they’re measuring. It’s a sweet start, but I think that having Twitter follower count and ratio at the center ends up a weakness: friend/follower counts are the pageviews of the social Web.
I will admit that these “pageviews” can be a very useful metric, and—probably more significant—that we haven’t worked out many other metrics for the social aspects of the Web, but nevertheless pageviews for Web sites begat site visits, unique visitors, visit frequency, average time on site, goal conversions, and any number of other metrics precisely because by themselves pageviews didn’t end up signifying much. Who were those pageviews, where were they coming from, what were they doing? Without context, a count of pageviews just isn’t substantial enough.
Since it’s pretty lame to complain without offering some kind of solution, I’ll offer my first thought: keep the follower data as a component of the algorithm, but add some context. The twittered links have a timestamp associated with them, so factor in visits to the linked hypem.com page and plays of the track in the 60 minutes following the tweet. If someone has a high follower count on Twitter but their followers don’t click through on or listen to their Hype Machine tweets, that person’s influence on the charts should decrease over time.
That’s certainly not a perfect answer (it doesn’t work on the first hypem.com tweet, becomes increasingly complicated if multiple people twitter a link around the same time, etc.), but some change along these lines would move the charts in a productive direction. Information is data placed into context, and follower count is data; find the right place for it and then we’re really in business here.