# Emojis Decoded: Leveraging ChatGPT for Enhanced Understanding in Social Media Communications

arXiv: https://arxiv.org/abs/2402.01681 · HTML: https://mlsanity.com/paper/2402.01681/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 73 references that are in the map.
- First posted on arXiv 22 Jan 2024.

## Builds on (most cited first, 22 of 22)

- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Exploiting Cloze Questions for Few Shot Text Classification and Natural Language Inference](https://mlsanity.com/paper/2001.07676/) (2020)
- [The Dawn of LMMs: Preliminary Explorations with GPT-4V(ision)](https://mlsanity.com/paper/2309.17421/) (2023)
- [TweetEval: Unified Benchmark and Comparative Evaluation for Tweet Classification](https://mlsanity.com/paper/2010.12421/) (2020)
- [GPT3Mix: Leveraging Large-scale Language Models for Text Augmentation](https://mlsanity.com/paper/2104.08826/) (2021)
- [Is ChatGPT better than Human Annotators? Potential and Limitations of ChatGPT in Explaining Implicit Hate Speech](https://mlsanity.com/paper/2302.07736/) (2023)
- [Using millions of emoji occurrences to learn any-domain representations for detecting sentiment, emotion and sarcasm](https://mlsanity.com/paper/1708.00524/) (2017)
- [ChatGPT-4 Outperforms Experts and Crowd Workers in Annotating Political Twitter Messages with Zero-Shot Learning](https://mlsanity.com/paper/2304.06588/) (2023)
- [Mementos: A Comprehensive Benchmark for Multimodal Large Language Model Reasoning over Image Sequences](https://mlsanity.com/paper/2401.10529/) (2024)
- [Explore Spurious Correlations at the Concept Level in Language Models for Text Classification](https://mlsanity.com/paper/2311.08648/) (2023)
- ["HOT" ChatGPT: The promise of ChatGPT in detecting and discriminating hateful, offensive, and toxic comments on social media](https://mlsanity.com/paper/2304.10619/) (2023)
- [Leveraging ChatGPT As Text Annotation Tool For Sentiment Analysis](https://mlsanity.com/paper/2306.17177/) (2023)
- [Spice up Your Chat: The Intentions and Sentiment Effects of Using Emoji](https://mlsanity.com/paper/1703.02860/) (2017)
- [Scalable Prompt Generation for Semi-supervised Learning with Language Models](https://mlsanity.com/paper/2302.09236/) (2023)
- [Emoji-Powered Representation Learning for Cross-Lingual Sentiment Classification](https://mlsanity.com/paper/1806.02557/) (2018)
- [Using Noisy Self-Reports to Predict Twitter User Demographics](https://mlsanity.com/paper/2005.00635/) (2020)
- [Human vs. LMMs: Exploring the Discrepancy in Emoji Interpretation and Usage in Digital Communication](https://mlsanity.com/paper/2401.08212/) (2024)
- [Emoji Promotes Developer Participation and Issue Resolution on GitHub](https://mlsanity.com/paper/2308.16360/) (2023)
- [EmojiLM: Modeling the New Emoji Language](https://mlsanity.com/paper/2311.01751/) (2023)
- [A First Look at Emoji Usage on GitHub: An Empirical Study](https://mlsanity.com/paper/1812.04863/) (2018)
- [Emojis predict dropouts of remote workers: An empirical study of emoji usage on GitHub](https://mlsanity.com/paper/2102.05737/) (2021)

## Built on by (most cited first, 7 of 7)

- [Irony in Emojis: A Comparative Study of Human and LLM Interpretation](https://mlsanity.com/paper/2501.11241/) (2025)
- [When Smiley Turns Hostile: Interpreting How Emojis Trigger LLMs' Toxicity](https://mlsanity.com/paper/2509.11141/) (2025)
- [From Adoption to Adaption: Tracing the Diffusion of New Emojis on Twitter](https://mlsanity.com/paper/2402.14187/) (2024)
- [Semantics Preserving Emoji Recommendation with Large Language Models](https://mlsanity.com/paper/2409.10760/) (2024)
- [Does Geo-co-location Matter? A Case Study of Public Health Conversations during COVID-19](https://mlsanity.com/paper/2405.17710/) (2024)
- [Unlocking Cross-Lingual Sentiment Analysis through Emoji Interpretation: A Multimodal Generative AI Approach](https://mlsanity.com/paper/2412.17255/) (2024)
- [The Hidden Language of Harm: Examining the Role of Emojis in Harmful Online Communication and Content Moderation](https://mlsanity.com/paper/2506.00583/) (2025)

## Abstract (arXiv)

Emojis, which encapsulate semantics beyond mere words or phrases, have become prevalent in social network communications. This has spurred increasing scholarly interest in exploring their attributes and functionalities. However, emoji-related research and application face two primary challenges. First, researchers typically rely on crowd-sourcing to annotate emojis in order to understand their sentiments, usage intentions, and semantic meanings. Second, subjective interpretations by users can often lead to misunderstandings of emojis and cause the communication barrier. Large Language Models (LLMs) have achieved significant success in various annotation tasks, with ChatGPT demonstrating expertise across multiple domains. In our study, we assess ChatGPT's effectiveness in handling previously annotated and downstream tasks. Our objective is to validate the hypothesis that ChatGPT can serve as a viable alternative to human annotators in emoji research and that its ability to explain emoji meanings can enhance clarity and transparency in online communications. Our findings indicate that ChatGPT has extensive knowledge of emojis. It is adept at elucidating the meaning of emojis across various application scenarios and demonstrates the potential to replace human annotators in a range of tasks.