# From Adoption to Adaption: Tracing the Diffusion of New Emojis on Twitter

arXiv: https://arxiv.org/abs/2402.14187 · HTML: https://mlsanity.com/paper/2402.14187/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 55 references that are in the map.
- First posted on arXiv 22 Feb 2024.

## Builds on (most cited first, 20 of 20)

- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [What Does BERT Look At? An Analysis of BERT's Attention](https://mlsanity.com/paper/1906.04341/) (2019)
- [ChatGPT Outperforms Crowd-Workers for Text-Annotation Tasks](https://mlsanity.com/paper/2303.15056/) (2023)
- [TweetEval: Unified Benchmark and Comparative Evaluation for Tweet Classification](https://mlsanity.com/paper/2010.12421/) (2020)
- [Using millions of emoji occurrences to learn any-domain representations for detecting sentiment, emotion and sarcasm](https://mlsanity.com/paper/1708.00524/) (2017)
- [Identifying and Mitigating Spurious Correlations for Improving Robustness in NLP Models](https://mlsanity.com/paper/2110.07736/) (2021)
- [Explore Spurious Correlations at the Concept Level in Language Models for Text Classification](https://mlsanity.com/paper/2311.08648/) (2023)
- [emoji2vec: Learning Emoji Representations from their Description](https://mlsanity.com/paper/1609.08359/) (2016)
- [CRAB: Assessing the Strength of Causal Relationships Between Real-world Events](https://mlsanity.com/paper/2311.04284/) (2023)
- [A Survey of Information Cascade Analysis: Models, Predictions, and Recent Advances](https://mlsanity.com/paper/2005.11041/) (2020)
- [Twitter Sentiment Analysis via Bi-sense Emoji Embedding and Attention-based LSTM](https://mlsanity.com/paper/1807.07961/) (2018)
- [SemEval-2015 Task 10: Sentiment Analysis in Twitter](https://mlsanity.com/paper/1912.02387/) (2019)
- [Emoji-Powered Representation Learning for Cross-Lingual Sentiment Classification](https://mlsanity.com/paper/1806.02557/) (2018)
- [Emojis Decoded: Leveraging ChatGPT for Enhanced Understanding in Social Media Communications](https://mlsanity.com/paper/2402.01681/) (2024)
- [Emoji Promotes Developer Participation and Issue Resolution on GitHub](https://mlsanity.com/paper/2308.16360/) (2023)
- [Text-Transport: Toward Learning Causal Effects of Natural Language](https://mlsanity.com/paper/2310.20697/) (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)
- [Tweet Insights: A Visualization Platform to Extract Temporal Insights from Twitter](https://mlsanity.com/paper/2308.02142/) (2023)

## Built on by (most cited first, 1 of 1)

- [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)

In the rapidly evolving landscape of social media, the introduction of new emojis in Unicode release versions presents a structured opportunity to explore digital language evolution. Analyzing a large dataset of sampled English tweets, we examine how newly released emojis gain traction and evolve in meaning. We find that community size of early adopters and emoji semantics are crucial in determining their popularity. Certain emojis experienced notable shifts in the meanings and sentiment associations during the diffusion process. Additionally, we propose a novel framework utilizing language models to extract words and pre-existing emojis with semantically similar contexts, which enhances interpretation of new emojis. The framework demonstrates its effectiveness in improving sentiment classification performance by substituting unknown new emojis with familiar ones. This study offers a new perspective in understanding how new language units are adopted, adapted, and integrated into the fabric of online communication.