发表机构
University of Illinois Urbana-Champaign; Toyota Technological Institute at Chicago（伊利诺伊大学厄巴纳-香槟分校; 芝加哥丰田技术研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究利用LLM标注Reddit俚语，结合社交网络与语言语境建模新词扩散，发现桥接资本促进采纳、结合资本抑制采纳，且广泛语境使用延缓正式采纳。
AI 中文摘要
近年来，新词在网络社区中的采纳与扩散重新受到关注。随着诸如 APT（指一首 K-pop 歌曲）等网络俚语和诸如 Canon Event（意为一个尴尬但关键的事件）等短语在网上病毒式传播，理解促成其成功的机制变得越来越重要。以往的研究往往要么从社会互动的角度，要么从俚语本身的语言学属性来解释俚语扩散，但很少同时从这两个角度出发。一个主要障碍是在大规模在线交流中标注俚语使用的高昂成本。然而，大型语言模型（LLMs）的最新进展使得将它们用作此类任务的可扩展标注器成为可能。在本研究中，我们首先构建了一个人工标注的基准，以评估 LLM 在检测真实 Reddit 交流中俚语使用的表现。然后，我们利用基于 LLM 的标注来建模俚语的采纳与扩散。我们的结果表明，具有更高桥接资本的俚语传播者与后续采纳的增加相关，而具有更高结合资本的传播者与采纳的减少相关。我们还发现，俚语更广泛的语境使用与新用户正式采纳前更长的等待时间相关。总之，这些发现表明，社交网络结构和语言语境共同塑造了新词在网络社区中的扩散。
英文摘要
Adoption and diffusion of neologisms in online communities have received renewed attention in recent years. As internet slang terms such as APT, referring to a K-pop song, and phrases such as Canon Event meaning an embarrassing but pivotal event, go viral online, it becomes increasingly important to understand the mechanisms that contribute to their success. Prior studies have often explained slang diffusion either from the perspective of social interaction or from the linguistic properties of the slang itself, but rarely from both perspectives together. One major obstacle has been the high cost of annotating slang usage in large-scale online communication. Recent advances in large language models (LLMs), however, make it possible to use them as scalable annotators for such tasks. In this study, we first curate a human-annotated benchmark to evaluate LLM performance in detecting slang usage in real Reddit communication. We then leverage LLM-based annotations to model slang adoption and diffusion. Our results show that slang diffusers with higher bridging capital are associated with increased subsequent adoption, whereas diffusers with higher bonding capital are associated with reduced adoption. We also find that wider contextual usage of a slang term is associated with a longer time before new users officially adopt it. Together, these findings suggest that both social-network structure and linguistic context shape the diffusion of neologisms in online communities.
CommentsAccepted to EMNLP 2026 main conference
