Through a Gender Lens: Learning Usage Patterns of Emojis from Large-Scale Android Users

ABSTRACT

Based on a large data set of emoji using behavior collected from smartphone users over the world, this paper investigates gender-specific usage of emojis. We present various interesting findings that evidence a considerable difference in emoji usage by female and male users. Such a difference is significant not just in a statistical sense; it is sufficient for a machine learning algorithm to accurately infer the gender of a user purely based on the emojis used in their messages. In real world scenarios where gender inference is a necessity, models based on emojis have unique advantages over existing models that are based on textual or contextual information. Emojis not only provide language-independent indicators, but also alleviate the risk of leaking private user information through the analysis of text and metadata.

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Index Terms

• Human-centered computing

  • Human computer interaction (HCI)

    • HCI design and evaluation methods

      • User models

• Information systems

  • Information systems applications

    • Data mining

• Social and professional topics

  • User characteristics

    • Gender