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

arXiv: https://arxiv.org/abs/1705.05546 · HTML: https://mlsanity.com/paper/1705.05546/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 May 2017.

## Built on by (most cited first, 5 of 5)

- [Irony in Emojis: A Comparative Study of Human and LLM Interpretation](https://mlsanity.com/paper/2501.11241/) (2025)
- [Identity Signals in Emoji Do not Influence Perception of Factual Truth on Twitter](https://mlsanity.com/paper/2105.03160/) (2021)
- [C3DRec: Cloud-Client Cooperative Deep Learning for Temporal Recommendation in the Post-GDPR Era](https://mlsanity.com/paper/2101.05641/) (2021)
- [How to Do Things without Words: Modeling Semantic Drift of Emoji](https://mlsanity.com/paper/2110.04093/) (2021)
- [FinMoji: A Framework for Emoji-driven Sentiment Analysis in Financial Social Media](https://mlsanity.com/paper/2605.09469/) (2026)

## Abstract (arXiv)

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.