We argue that classic citation-based scientific document clustering approaches, like co-citation or Bibliographic Coupling, lack to leverage the social-usage of the scientific literature originate through online information dissemination platforms, such as Twitter. In this paper, we present the methodology Tweet Coupling , which measures the similarity between two or more scientific documents if one or more Twitter users mention them in the tweet(s). We evaluate our proposal on an altmetric dataset, which consists of 3081 scientific documents and 8299 unique Twitter users. By employing the clustering approaches of Bibliographic Coupling and Tweet Coupling, we find the relationship between the bibliographic and tweet coupled scientific documents. Further, using VOSviewer, we empirically show that Tweet Coupling appears to be a better clustering methodology to generate cohesive clusters since it groups similar documents from the subfields of the selected field, in contrast to the Bibliographic Coupling approach that groups cross-disciplinary documents in the same cluster.

中文翻译：

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Tweet Coupling：一种用于科学出版物聚类的社交媒体方法

我们认为，经典的基于引文的科学文献聚类方法，如共引或书目耦合，缺乏利用源自在线信息传播平台（如 Twitter）的科学文献的社会用途。在本文中，我们介绍了 Tweet Coupling 方法，如果一个或多个 Twitter 用户在推文中提及两个或多个科学文档，则该方法会衡量两个或多个科学文档之间的相似性。我们在 altmetric 数据集上评估我们的提议，该数据集由 3081 份科学文档和 8299 个唯一的 Twitter 用户组成。通过采用书目耦合和推文耦合的聚类方法，我们找到了书目和推文耦合的科学文献之间的关系。此外，使用 VOSviewer，