# Irony in Emojis: A Comparative Study of Human and LLM Interpretation

arXiv: https://arxiv.org/abs/2501.11241 · HTML: https://mlsanity.com/paper/2501.11241/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 22 references that are in the map.
- First posted on arXiv 20 Jan 2025.

## Builds on (most cited first, 11 of 11)

- [Generative Agents: Interactive Simulacra of Human Behavior](https://mlsanity.com/paper/2304.03442/) (2023)
- ["Kelly is a Warm Person, Joseph is a Role Model": Gender Biases in LLM-Generated Reference Letters](https://mlsanity.com/paper/2310.09219/) (2023)
- [Gender bias and stereotypes in Large Language Models](https://mlsanity.com/paper/2308.14921/) (2023)
- [Spice up Your Chat: The Intentions and Sentiment Effects of Using Emoji](https://mlsanity.com/paper/1703.02860/) (2017)
- [Studying Cultural Differences in Emoji Usage across the East and the West](https://mlsanity.com/paper/1904.02671/) (2019)
- [Human vs. LMMs: Exploring the Discrepancy in Emoji Interpretation and Usage in Digital Communication](https://mlsanity.com/paper/2401.08212/) (2024)
- [Emojis Decoded: Leveraging ChatGPT for Enhanced Understanding in Social Media Communications](https://mlsanity.com/paper/2402.01681/) (2024)
- [Through a Gender Lens: Learning Usage Patterns of Emojis from Large-Scale Android Users](https://mlsanity.com/paper/1705.05546/) (2017)
- [On the Context-Free Ambiguity of Emoji](https://mlsanity.com/paper/2201.06302/) (2022)
- [Mining the Relationship between Emoji Usage Patterns and Personality](https://mlsanity.com/paper/1804.05143/) (2018)
- [Semantics Preserving Emoji Recommendation with Large Language Models](https://mlsanity.com/paper/2409.10760/) (2024)

## Built on by (most cited first, 6 of 6)

- [Cognitive Mismatch in Multimodal Large Language Models for Discrete Symbol Understanding](https://mlsanity.com/paper/2603.18472/) (2026)
- [When Smiley Turns Hostile: Interpreting How Emojis Trigger LLMs' Toxicity](https://mlsanity.com/paper/2509.11141/) (2025)
- [V-VAE: A Variational Auto Encoding Framework Towards Fine-Grained Control over Human-Like Chat](https://mlsanity.com/paper/2506.01524/) (2025)
- [False Friends in the Shell: Unveiling the Emoticon Semantic Confusion in Large Language Models](https://mlsanity.com/paper/2601.07885/) (2026)
- [Irony Detection in Urdu Text: A Comparative Study Using Machine Learning Models and Large Language Models](https://mlsanity.com/paper/2510.22356/) (2025)
- [Evaluating Bias in Chinese Large Language Models across Tasks and Registers: Task Operationalization Effects and Irony Classification Risks](https://mlsanity.com/paper/2610.14464/) (2026)

## Abstract (arXiv)

Emojis have become a universal language in online communication, often carrying nuanced and context-dependent meanings. Among these, irony poses a significant challenge for Large Language Models (LLMs) due to its inherent incongruity between appearance and intent. This study examines the ability of GPT-4o to interpret irony in emojis. By prompting GPT-4o to evaluate the likelihood of specific emojis being used to express irony on social media and comparing its interpretations with human perceptions, we aim to bridge the gap between machine and human understanding. Our findings reveal nuanced insights into GPT-4o's interpretive capabilities, highlighting areas of alignment with and divergence from human behavior. Additionally, this research underscores the importance of demographic factors, such as age and gender, in shaping emoji interpretation and evaluates how these factors influence GPT-4o's performance.