Through a Gender Lens: Learning Usage Patterns of Emojis from Large-Scale Android Users
This provides a language-independent and privacy-preserving method for gender inference in real-world applications, though it is incremental over existing text-based models.
The paper investigated gender-specific emoji usage patterns from a global smartphone dataset and found significant differences, enabling machine learning models to accurately infer user gender based solely on emojis.
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.