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

TL;DR

This study analyzes gender-specific emoji usage patterns from a large global dataset, demonstrating that emoji behavior can accurately predict user gender and offers privacy advantages over text-based models.

Contribution

It introduces a large-scale analysis of gender differences in emoji usage and shows that emoji-based models can effectively infer gender while preserving user privacy.

Findings

Gender differences in emoji usage are statistically significant.

Emoji-based models can accurately predict user gender.

Emoji usage offers privacy advantages over text-based models.

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.