From Adoption to Adaption: Tracing the Diffusion of New Emojis on Twitter
This research addresses the problem of understanding digital language evolution for social media analysts and linguists, but it is incremental as it builds on existing studies of emoji diffusion.
The study tackled the problem of how new emojis are adopted and evolve in meaning on Twitter, finding that early adopter community size and semantics are key to popularity, with some emojis shifting in sentiment associations, and proposed a framework that improves sentiment classification by substituting unknown emojis with familiar ones.
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.