Analysing Biases in Human Perception of User Age and Gender from Text

Citation:
Flekova, Lucie, Jordan Carpenter, Salvatore Giorgi, Lyle Ungar, and Daniel Preoţiuc-Pietro. Analysing Biases in Human Perception of User Age and Gender from Text. ACL., 2016.

Abstract:

User traits disclosed through written text, such as age and gender, can be used to personalize applications such as recommender systems or conversational agents. However, human perception of these traits is not perfectly aligned with reality. In this paper, we conduct a large-scale crowdsourcing experiment on guessing age and gender from tweets. We systematically analyze the quality and possible biases of these predictions. We identify the textual cues which lead to miss-assessments of traits or make workers more or less confident in their choice. Our study demonstrates that differences between real and perceived traits are noteworthy and elucidates inaccurately used stereotypes in human perception.

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