Towards a typology of hashtag publics: a large-scale comparative study of user engagement across trending topics

ABSTRACT

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.

KEYWORDS:

• Twitter
• hashtags
• publics
• acute events
• social media

Acknowledgements

A number of additional hashtag datapoints for our analysis were provided by members of the Association of Internet Researchers community in response to an open call for crowdsourced contributions. We thank our colleagues for their contributions, and acknowledge them here as contributing authors.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the Australian Research Council through ARC Future Fellowship grant Understanding Intermedia Information Flows in the Australian Online Public Sphere, and by infrastructure provided through the Australian Research Council LIEF project Tracking Infrastructure for Social Media Analysis.

Notes on contributors

Axel Bruns

Prof Axel Bruns is an Australian Research Council Future Fellow and Professor in the Digital Media Research Centre at Queensland University of Technology in Brisbane, Australia. He is the author of Blogs, Wikipedia, Second Life and Beyond: From Production to Produsage (2008) and Gatewatching: Collaborative Online News Production (2005), and a co-editor of the Routledge Companion to Social Media and Politics (2016), Twitter and Society (2014), A Companion to New Media Dynamics (2012), and Uses of Blogs (2006). His current work focusses on the study of user participation in social media spaces such as Twitter, and its implications for our understanding of the contemporary public sphere, drawing especially on innovative new methods for analysing ‘big social data’. His research blog is at http://snurb.info/, and he tweets at @snurb_dot_info. See http://mappingonlinepublics.net/ for more details on his research into social media.

Brenda Moon

Brenda Moon is a Postdoctoral Research Fellow in the Digital Media Research Centre at Queensland University of Technology. Her primary research interest is in using interdisciplinary approaches to apply and develop digital methods. She is currently exploring the application of Fourier analysis to a variety of social data. Brenda’s PhD research investigated the use of social media to monitor public discussion of science through her thesis ‘Scanning the Science – Society Horizon: Using Social Media to Monitor Public Discussion of Science’. Brenda actively promotes Open Culture, particularly maker spaces, open-source software, open hardware, and open science, and supports women in computing through participation in groups including PyLadies, AdaCamp, and DjangoGirls.

Avijit Paul

Dr. Avijit Paul is an early career researcher and developer based in the Digital Media Research Centre at Queensland University of Technology in Brisbane, Australia. His research explores social media from humanitarian perspectives. In his projects, he utilises machine learning approaches to find patterns in the data. After completing his PhD, he joined the QUT-led TrISMA project as a Data Analysis Tools Developer.

Felix Münch

Felix Victor Münch is a PhD Candidate in the Digital Media Research Centre at Queensland University of Technology. With a BSc in Physics (LMU, Munich, Germany), an MA in Journalism (LMU and German Journalist School, Munich, Germany), and work experience in online media brand communication as an online media concepter and strategist, his main fields of interest are network science methods and online media