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COP26/27 Engagement on Twitter: An Analysis Using the Practice Mapping Approach (SM&S 2026)

Snurb — Sunday 30 August 2026 20:08
Politics | Polarisation | ‘Fake News’ | Social Media | Practice Mapping | Twitter | SM&S 2026 |

Axel Bruns, Ehsan Dehghan, Michelle Riedlinger, Carly Lubicz-Zaorski, Laura Vodden, Daniel Angus, and Samantha Vilkins

  • 15 July 2026 – Social Media & Society conference, Glasgow

Presentation Slides

cop26-27-engagement-on-twitter-an-analysis-using-the-practice-mapping-approach from Axel Bruns

Abstract

Major international gatherings such as the COP26 and COP27 climate summits usually attracted considerable global engagement on social media, especially on Twitter (before the considerable downturn in active use following its acquisition by Elon Musk). Conventional analysis of the hashtag or keyword datasets gathered for the public discussion of such events often produces only relatively limited and predictable insights, however: much of the public debate tends to be centered around a handful of major, widely @mentioned and retweeted accounts (including official event, host, and UN accounts), uses the generic event hashtags, and represents well-known groups of activists and lobbyists. None of this is surprising. In this paper, we advance our analysis beyond such predictable outcomes by applying a novel analytical approach called practice mapping, which instead of building on an interaction network analysis approach investigates similarities and disjunctures between the distinct posting practices of participants in the datasets for the two events. This still takes into account patterns in whom accounts @mention and retweet, and how they are addressed in return, but also adds further activity patterns that relate to their distinct uses of hashtags, URLs, and language choices. Drawing on this expanded range of activity patterns, the practice mapping approach detects a broader range of distinctive participation practices within the COP26 and COP27 debates on Twitter, enabling us to identify and interpret a more diverse set of participant groups within the datasets.

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