We hypothesize that this is due to two reasons.
we tokenize with the NLTK's TweetTokenizer (Bird, 2006), lowercase all text, and use regular expressions to remove stop words, numbers, urls, consecutive repeated words and Twitter users (i.e., tokens whose first character is '@').