Why Gender and Age Prediction from Tweets is Hard: Lessons from a Crowdsourcing Experiment

There is a growing interest in automatically predicting the gender and age of authors from texts. However, most research so far ignores that language use is related to the social identity of speakers, which may be different from their biological identity. In this paper, we combine insights from sociolinguistics with data collected through an online game, to underline the importance of approaching age and gender as social variables rather than static biological variables. In our game, thousands of players guessed the gender and age of Twitter users based on tweets alone. We show that more than 10% of the Twitter users do not employ language that the crowd associates with their biological sex. It is also shown that older Twitter users are often perceived to be younger. Our findings highlight the limitations of current approaches to gender and age prediction from texts.