KDD 2007, which I very much enjoyed, ended with a great invited talk from Chris Anderson. The talk was essentially a summary of his book, The Long Tail, but with a bias towards describing the details of the data rather than the consequences of the vision. One key update that was present in his talk was a discussion of the relationship and differences between power law distributions and lognormal distributions. In fact, Chris admitted that he wasn't really sure if the phenomena he observed and described in his book as having power law distributions really did have these behaviours or if they were actually better captured by a lognormal model. However, he recovered graceful by pointing out that for 50% of the distribution (from the head) there wasn't much difference and so much of the conclusions in the book would hold.
Chris has actually posted on this topic in detail on his blog (a while back).
ICWSM is full of papers with power law distributions in them. However, it should be noted that it wasn't uncommon for presenters to be questioned on the fit of the data to such a law and to have it be suggested that they may in fact be lognormal distributions.