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.
@article{arxiv.1705.05546,
title = {Through a Gender Lens: Learning Usage Patterns of Emojis from Large-Scale Android Users},
author = {Zhenpeng Chen and Xuan Lu and Wei Ai and Huoran Li and Qiaozhu Mei and Xuanzhe Liu},
journal= {arXiv preprint arXiv:1705.05546},
year = {2018}
}