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

TL;DR

This study investigates how new emojis spread and change in meaning on Twitter, highlighting the roles of early adopters and semantics, and introduces a language model-based framework to interpret and improve sentiment analysis of new emojis.

Contribution

It presents a novel framework using language models to analyze emoji diffusion and semantics, enhancing understanding and sentiment classification of new emojis on social media.

Findings

Community size of early adopters influences emoji popularity.

Emojis undergo semantic and sentiment shifts during diffusion.

Language models improve sentiment analysis by contextual substitution of emojis.

Abstract

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.