Here, from the paper, is a table breaking down how various combinations of data helped boost the gender prediction success rate.
Mitre found that given certain characters or combinations of characters, the computer could wisely bet on the gender of the tweeter. The mere fact of a tweet containing an exclamation mark or a smiley face meant that odds were a woman was tweeting, for instance. Of the most gender-skewed words, the majority were in the female category, while only a few were male, leading to this unintentionally hilarious figure from the Mitre paper:
A case for my book: biases define identities, which leave traces everywhere!