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Going meta: Norm formation and enactment on the stack exchange network

Matthews, Paul



This paper presents research into the way norms are developed, expressed and enforced on sites that are part of the Stack Exchange (SE) social question-answering network. This network has a number of topical knowledge exchange communities using similar underlying software, enabling a focus on variation in social design.

SE also separates community-related discussion from topic specific content through the use of its “Meta” sub-sites. These were analysed together with their main sites for variation in the development and enforcement of norms. Norms expressed through explicit community policies seem rather less important than those embodied in busy discussion threads on the Meta sites. While Meta participation was fairly uniform across communities, different emphasis on scope and quality led to variation in Meta discussion and the way that norms were enacted through question closures.

The social distribution of moderation work was also uneven between sites, with some sites having a few highly active moderators involved in question closure. The level of closures across the sites studied did not seem to significantly discourage participation.

Indeed, modelling the effect of closures on quality and engagement indicated that low levels of closure enable “legitimate peripheral participation”, the process by which newcomers can become inducted and make contributions of increasing quality over time.

Presentation Conference Type Conference Paper (unpublished)
Start Date Apr 11, 2016
Publication Date Jan 1, 2016
Peer Reviewed Peer Reviewed
APA6 Citation stack exchange network. Paper presented at World Wide Web Conference 2016, Workshop on the Theory and Practice of Social Machines
Keywords norms, online communities, social machines, knowledge exchange, social question-answering
Publisher URL
Additional Information Title of Conference or Conference Proceedings : World Wide Web Conference 2016, Workshop on the Theory and Practice of Social Machines