"By scanning for patterns in all the tweets of a given user, Mitre’s program was able to guess the correct gender 75.8% of the time—a 20% improvement over the baseline. And even just by analyzing a single tweet of a user, it was right 65.9% of the time—an over 10% improvement over the baseline…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"

"By scanning for patterns in all the tweets of a given user, Mitre’s program was able to guess the correct gender 75.8% of the time—a 20% improvement over the baseline. And even just by analyzing a single tweet of a user, it was right 65.9% of the time—an over 10% improvement over the baseline…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"

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