Temporal semantics: Time-varying hashtag sense clustering

Abstract

Hashtags are creative labels used in micro-blogs to characterize the topic of a message/discussion. However, since hashtags are created in a spontaneous and highly dynamic way by users using multiple languages, the same topic can be associated to different hashtags and conversely, the same hashtag may imply different topics in different time spans. Contrary to common words, sense clustering for hashtags is complicated by the fact that no sense catalogues are available, like, e.g. Wikipedia or WordNet and furthermore, hashtag labels are often obscure. In this paper we propose a sense clustering algorithm based on temporal mining. First, hashtag time series are converted into strings of symbols using Symbolic Aggregate ApproXimation (SAX), then, hashtags are clustered based on string similarity and temporal co-occurrence. Evaluation is performed on two reference datasets of semantically tagged hashtags. We also perform a complexity evaluation of our algorithm, since efficiency is a crucial performance factor when processing large-scale data streams, such as Twitter.