Well, it depends on how you look at it. The Bayesian MAP approach is basically the same as the frequentist approach with made up data. Instead of using the maximum likelihood estimator p = pos/(pos+neg) you pretend that each new post already has some up/down votes by default, and then you use simply p = pos/(pos+neg). Seems like an even more intuitive explanation of the Bayesian knob to me! Rather than an abstract "alpha" to a "Dirichlet prior", you get something concrete (the number of made up votes). And you get a simple formula, which some would find desirable.

But I agree that the Bayesian approach is conceptually much cleaner. IMO the frequentist approach is just computational corner cutting for when the math in the Bayesian approach gets too involved, which is sometimes useful. What's nice about the Bayesian approach is that you state your assumptions and then it's just turning the math machinery. In contrast, in the frequentist approach the assumptions are interwoven and hidden in arbitrary choices in how the math is done (And then they claim that Bayesians are subjective! It's just that Bayesians admit that they are subjective. Frequentists try to hide the fact that they are more subjective in the math). The not so nice thing is that turning the math machinery is not always so easy and does not always produce fast algorithms. That's where maximum likelihood and friends come in, but I'd view them as an approximation to Bayesian methods.