Human-interpretable clustering of short-text using large language models

Short text is playing an increasingly important role in human expression and interaction, due to the widespread use of social media platforms and messaging services such as X (formerly Twitter), Weibo, WhatsApp, Instagram and Reddit. The enormous quantities of data produced by users of these platforms holds the promise of not just real-time identification of events [1] and current opinions [2], but also a deeper understanding of the drivers of information flow between the users [3]. A first step in engaging with the large data sets is to typically reduce the complexity, by clustering the text data into similar groups [4]. However, short text clustering is challenging, due to the limited contextual information available in a single piece of text, and the low incidence of word co-occurrence between pieces of text [5, 6]. The possible applications of success has led to a focus in the machine learning community on clustering, with an increasing number of methods developed to provide a deeper understanding of large collections of short text data [7, 8].