Dataset for Named Entity Recognition on Informal Text
As I understand it, these are the properties that you're seeking in a sample dataset:


Text data
It should be informal, i.e. have typos, slang, and basically something not professionally edited 
Something other than Twitter (I don't blame you, Twitter is a useful yet way overused example datasource in text mining)


Here are some recommendations:


Emails from the SpamAssassin corpus -- note that both "ham" (non-spam) and spam datasets are available
microblogPCU data set from UCI, which is data scraped from the microblogs of Sina Weibo users -- note, the raw text data is a mix of Chinese and English (you could perform machine translation of the Chinese, filter to only English, or use it as-is)
Amazon Commerce reviews dataset from UCI
Within the bag-o-words dataset, try using the Enron emails
The Twenty Newsgroups dataset
This nice collection of SMS spam
You can always scrape (extract) your own text data from the Internet; I'm not sure which language or statistical package you're using, but XPath-based packages are available in R (rvest, scrapeR, etc) and Python to accomplish this