Equally sized topics in Latent Dirichlet allocation
It is normal, the best explantation I found for it, is from physics. Since Gibbs Sampling was known in physics long before LDA and LDA can simply be seen as a kind of matrix factorization. 
There is a system which has particles (words) and the particles can be in different states (topics). States with lower energy have a higher probability of being occupied than states with higher energy. 
Or simply: Since LDA is a dimensity reduction method that just compresses a words x documents matrix into a topics x documents and a words x topics matrix, the most effective way to do it is by maximizing the entropy which is done if the clusters get equal size. 

Just noticed that you can simply derive the above assumption from the dirichlet distribution itself: Take a look at the density (PDF) function and ignore the normalizing factor at the front and only assume symmetric alpha parameter. This results in a product term of X1...XK, that product term gets it's maximum if all X1...XK's have equal size, since the sum X1+X2+...+XK = 1, it's the same case as entropy above.