Emojis Decoded: Leveraging ChatGPT for Enhanced Understanding in Social Media Communications - Zhou, Yuhang; Xu, Paiheng; Wang, Xiyao; Lu, Xuan; Gao, Ge; Ai, Wei (Proceedings of the International AAAI Conference on Web and Social Media, 2025)
Emojis, which encapsulate semantics beyond 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 a 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 emoji-annotated and downstream tasks. Our objective is to validate the hypothesis that ChatGPT can serve as an 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 explaining the meaning of emojis across various application scenarios and demonstrates the potential to replace human annotators in a range of tasks.