While I agree with the spirit of the article, this is one of those cases where a Bayesian treatment is conceptually much clearer. Assume that ratings are being generated by a stable stochastic process where the underlying distribution is multinomial (ignoring the ordinal character of ratings, for the time being) and use a dirichlet conjugate prior. This gives you a posterior distribution over new ratings for an item.…
Or you could do a semi-frequentist thing and simplify your math by using MAP estimates to rank. Basically instead of score = #pos/(#pos + #neg), it becomes score = (#pos+x)/(#pos+x + #neg+y), where you choose x and y to suit your needs. You could choose x/y in proportion to the average number of up/down votes on your site or you could even choose x/y in proportion to the average number of up/down votes of the author of the post. That would rank posts of trolls lower than posts of good users. By varying x and y you can tweak the strength of this effect. You can interpret this as giving each item by default x upvotes and y downvotes.
This certainly works much better than the formula in the article. For example if a post has 1 upvote and 2 downvotes, his formula will say that should be ranked lower than a post with 1000 upvotes and 2000 downvotes (because he's using the lower bound of the confidence interval). Obviously that's bad because while the first post could be a good one, we know for certain that the second one isn't. In general his method will rank posts with a low number of votes very low, even compared to posts with a high number of downvotes.