Twitter’s hashtag functionality is now used for a very wide variety of purposes, from covering crises and other breaking news events through gathering an instant community around shared media texts (such as sporting events and TV broadcasts) to signalling emotive states from amusement to despair. These divergent uses of the hashtag are increasingly recognised in the literature, with attention paid especially to the ability for hashtags to facilitate the creation of ad hoc or hashtag publics. A more comprehensive understanding of these different uses of hashtags has yet to be developed, however. Previous research has explored the potential for a systematic analysis of the quantitative metrics that could be generated from processing a series of hashtag datasets. Such research found, for example, that crisis-related hashtags exhibited a significantly larger incidence of retweets and tweets containing URLs than hashtags relating to televised events, and on this basis hypothesised that the information-seeking and sharing behaviours of Twitter users in such different contexts were substantially divergent. This article updates such study and their methodology by examining the communicative metrics of a considerably larger and more diverse number of hashtag datasets, compiled over the past five years. This provides an opportunity both to confirm earlier findings, as well as to explore whether hashtag use practices may have shifted subsequently as Twitter’s userbase has developed further; it also enables the identification of further hashtag types beyond the “crisis” and “mainstream media event” types outlined to date. The article also explores the presence of such patterns beyond recognised hashtags, by incorporating an analysis of a number of keyword-based datasets. This large-scale, comparative approach contributes towards the establishment of a more comprehensive typology of hashtags and their publics, and the metrics it describes will also be able to be used to classify new hashtags emerging in the future. In turn, this may enable researchers to develop systems for automatically distinguishing newly trending topics into a number of event types, which may be useful for example for the automatic detection of acute crises and other breaking news events.