Aditya Pal from U Minn and I are presenting a paper at the WSDM 2011 conference on topical authority finding in Twitter.
Paper here: http://research.microsoft.com/en…
This is conceptually a PeopleRank for topics in Twitter, though it doesn’t work statistically like pagerank. Instead, we probabilisticly cluster people over a set of metrics and then heuristically pick the cluster with the most authoritative users (and then rank within that cluster). This is much less expensive to compute and can avoid your results being dominated by users with heavy network properties (i.e., celebrities who have orders of magnitude more followers, but aren’t necessarily experts)..
The dozen or so features we compute for each user are in the paper, and inlcude some things like ‘do those around you also tweet on this topic’, do you tend to tweet before or after others on this topic’, ‘do you always tweet on this topic or was this a one-off’.Some of these features are really helpful for weeding out spammers – like if your ‘self-similarity’ score is crazy high, you are probably a spammer (and also wouldn’t get clustered with legitimate authors).Overall it seems to work pretty well. There are example results in the paper.
For Twitter, there’s also a paper that is a more straightforward application of pagerank (here: http://www.sciweavers.org/publications/twitterrank-finding-topic-sensitive-influential-twitterers)..