From Adoption to Adaption: Tracing the Diffusion of New Emojis on Twitter
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866929251495706624 |
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| author | Zhou, Yuhang Lu, Xuan Ai, Wei |
| author_facet | Zhou, Yuhang Lu, Xuan Ai, Wei |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2402_14187 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | From Adoption to Adaption: Tracing the Diffusion of New Emojis on Twitter Zhou, Yuhang Lu, Xuan Ai, Wei Computers and Society Artificial Intelligence Computation and Language Human-Computer Interaction 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. |
| title | From Adoption to Adaption: Tracing the Diffusion of New Emojis on Twitter |
| topic | Computers and Society Artificial Intelligence Computation and Language Human-Computer Interaction |
| url | https://arxiv.org/abs/2402.14187 |