The top five reasons for lurking: improving community experiences for everyone

Preece, Jennifer; Nonnecke, Blair; Andrews, Dorine

doi:10.1016/j.chb.2003.10.015

Cited by 781 publications

(635 citation statements)

References 6 publications

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“…Only seven of the 100 members who posted the most in the space (with 1327 -9688 posts each, collectively accounting for 36% of the total posts, as of 28 February 2010) listed their gender as male. As with many online fan spaces, a small group of SWH members contributed the majority of the posts, indicating that many members lurk, or spend time on the site without posting messages (Preece, Nonnecke, and Andrews 2004). In fact, nearly 9000 SWH members did not make even a single post in the space.…”

Section: Methodsmentioning

confidence: 99%

Fangirls as teachers: examining pedagogic discourse in an online fan site

Lammers

2013

Learning, Media and Technology

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Section: Methodsmentioning

confidence: 99%

Fangirls as teachers: examining pedagogic discourse in an online fan site

Lammers

2013

Learning, Media and Technology

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“…Other approaches for designing notification messages can be considered, implemented and validated using the current framework. Social theories can inform the design of persuasive, motivational, and incentive driven messages that can influence members to contribute to the community and help them see the added value of their participation (Cialdini, 1993;Kollock, 1999;Preece et al, 2004;Rafaeli et al, 2004;Preece, 2009). It is more straightforward to facilitate participation in a community when some reward mechanism linked to participation can be given, e.g.…”

Section: Interference and Motivationmentioning

confidence: 99%

Adaptive notifications to support knowledge sharing in close-knit virtual communities

Kleanthous

Dimitrova

2012

User Model User-Adap Inter

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Social web-groups where people with common interests and goals communicate, share resources, and construct knowledge, are becoming a major part of today's organisational practice. Research has shown that appropriate support for effective knowledge sharing tailored to the needs of the community is paramount. This brings a new challenge to user modelling and adaptation, which requires new techniques for gaining sufficient understanding of a virtual community and identifying areas where the community may need support. The research presented here addresses this challenge presenting a novel computational approach for community-tailored support underpinned by organisational psychology and aimed at facilitating the functioning of the community as a whole (i.e. as an entity). A framework describing how key community processes -transactive memory, shared mental models, and cognitive centrality -can be utilised to derive knowledge sharing patterns from community log data is described. The framework includes two parts: (i) extraction of a community model that represents the community based on the key processes identified and (ii) identification of knowledge sharing behaviour patterns that are used to generate adaptive notifications. Although the notifications target individual members, they aim to influence individuals' behaviour in a way that can benefit the functioning of the community as a whole. A validation study has been performed to examine the effect of community-adapted notifications on individual members and on the community as a whole using a close-knit community of researchers sharing references. The study shows that notification messages can improve members' awareness and perception of how they relate to other members in the community. Interesting observations have been made about the linking between the physical and the virtual community, and how this may influence members' awareness and knowledge sharing behaviour. Broader implications for using log data to derive community models based on key community processes and generating community-adapted notifications are discussed.

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“…Bas) and posters (e.g. Jessica) (Preece et al 2004). Second, specific roles should be recognisable to the community members to avoid problems during the knowledge sharing process due to confusion about who is knowledgeable and who is not (Greenwood et al 1989).…”

Section: Knowledge Dating In Ad-hoc Transient Communitiesmentioning

confidence: 99%

“…A lurker, by definition, belongs to a community but never posts in it. The percentage of lurkers in established communities is very variable (i.e., ranging from 0% to 99%; (Preece et al 2004)). For example, lurkers appear to make up 45.5% of health support communities while the lurker population in software support communities could be as high as 82% (for an overview, see Preece et al 2004).…”

Section: Heterogeneitymentioning

confidence: 99%

“…The percentage of lurkers in established communities is very variable (i.e., ranging from 0% to 99%; (Preece et al 2004)). For example, lurkers appear to make up 45.5% of health support communities while the lurker population in software support communities could be as high as 82% (for an overview, see Preece et al 2004). Reasons for not posting range from 'didn't need to post', 'needed to find out about the group', "couldn't make the software work", "didn't like the group" to 'had nothing to offer' (Preece et al 2004).…”

Section: Heterogeneitymentioning

confidence: 99%

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