Posted by: soniahs | November 22, 2010

Exam readings: what makes an online community?

“Community” is a well-discussed term in the online realm- are online communities really communities? If so, how do they work (e.g., language-based, activity-based, social network-based)? And what is a community, anyway (village, workplace, civic organization, fandom)?

One of the issues that’s come up in my readings is how varying definitions of what a community is interact with different frameworks for how learning occurs. For example, the “communities of practice” framework says that people learn as an effect of the process of becoming members of a community. How a concept like this intersects with online communities, without the kind of face-to-face interactions that characterize traditional communities, is an interesting question. These three readings touch on this in varying ways.

Steven Brint. “Gemeinschaft Revisited: A Critique and Reconstruction of the Community Concept.” Sociological Theory 19(1): 1-23, 2001.

Summary: The concept of “community” is fuzzy and has fallen out of favor in sociology; replaced by oversimplified ideas of “interaction rituals,” social networks (focus on material benefits to participants), and social capital (focus on motives). Brint proposes a new definition of community: an aggregate of people with common activities & beliefs, bound together principally by values, concerns, affect & loyalty. The motives for interaction are central (though rational or financial motives can be a part, the ones listed are primary-work or interest groups/clubs mainly bound by rational means, so not communities), and groups can be any size, or dispersed. He provides a framework for differentiating subtypes at different levels of interaction: 1) ultimate context (geographic or choice-based), 2) primary motivation (activity or belief-based), and 3) either frequency of interaction (for geog. communities) or location of other members (for choice communities; dispersed groups here get 4th level of interaction, depending on whether they ever meet in person). The key is that these organizational features predict organization & “climate” features of the different types of communities (though he states that these are hypotheses), e.g., monitoring, levels of investment, pressure for conformity. However, there are factors of environmental context (e.g., geography, tolerance as a norm) and community-building (e.g., hazing, meeting places, enforced appearance) that will also be important in shaping communities.

Comments: Discusses the implications of this framework for liberal vs. socialist models of community; suggests that “community” persists as an ideal even though our typical experiences of it tend to be non-egalitarian and non-validating. However, he speculates that virtual or “imaginary” communities, experiences are closer to this ideal of egalitarian & validating community; perhaps these communities will be freer of vice and less judgmental of members. I’m not sure how this last idea really holds up in online communities- there’s certainly a lot of demoninzing of the “other” that goes on online…

Links to: Lave & Wenger (community/participatory knowledge)

Molly M. Wasco, Samer Faraj, and Robin Teigland. “Collective Action and Knowledge Contribution in Electronic Networks of Practice.” Journal for the Association of Information Systems. 5(11-12): 494-513, 2004.

Summary: The authors describe a model for “electronic networks of practice:” informal groups that primarily exchange information online. They call ENPs a special case of communities of practice, in which there are no formal controls and participation isn’t face to face (CPs would lie on the other end of a continuum of such groups). One distinction in ENPs is that individuals’ use of collective knowledge is “non-rival” and “non-excludable” (though individuals can “free-ride” on others’ contributions). In their model, macrostructural properties (e.g., medium of communication, network size and access) determine structural ties (generalized patterns of exchange- generally non-reciprocal bet. individuals). Structural ties affect the relational strength of ties (e.g., obligation, identification, trust-between network as a whole and individuals); these influence creation of understanding and community norms. Relative strength of ties affects both social controls (reputation, status, flaming, shunning, banning) and knowledge contribution. Knowledge contribution both influences and is influenced by individual motivations and resources; it also feeds back onto structural ties (this is the mechanism for re-creating, strengthening, and expanding the network).

Comments: The authors end with discussion of the model’s limitations (e.g., need to make modifications if there are F2F interactions, formal incentives for participation, reciprocal relationships that develop over time). They also see a need for looking at individual roles-many times, an active core of participants does most of the work.

Links to: Lave & Wenger (communities of practice); Preece & Schneiderman (discussion of process of enrollment & individual participation)

Jennifer Preece and Ben Shneiderman. “The Reader-to-Leader Framework: Motivating Technology-Mediated Social Participation.” Transactions on Human-Computer Interaction. 1(1): 13-32, 2009.

Summary: Describes how people get involved in social media by gradually increasing the extent of their participation. The authors’ framework tries to incorporate various related areas of research with the goal of providing a unifying framework for future research. At each of the successive stages of participation (reader, contributor, collaborator, leader), numbers of participants decrease; people can also jump stages, move backwards, or terminate participation. Readers can be attracted with ads, word of mouth; good interface design and reading user-generated content keep them coming back. Contributors add to the communal effort without the intention of getting too involved. Reputation systems (with communal ranking or tagging) and ethos garnered from association with credible figures help drive increasing participation.  Collaborators develop common ground with others and work on mutual creations (short or long-term). Satisfying discussions, building social capital, collectivism are all contributing factors. Leaders promote, mentor, set policy; they need good editing & synthesis tools, recognition, and opportunities to contribute meaningfully. Well-defined and focused groups are likely to have stronger group identity & participation. Final section focuses on future research needs (e.g., research at each stage, metrics for assessment).

Comments: Authors suggest using data logging/tracking for research, which has ethical implications. Also suggest that young people care less about privacy; I’m not sure this is a generational shift or just young people being dumb.

Links to: Lave & Wenger (LPP); Von Ahn & Dabbish (getting people involved with GWPs); Howe (crowdsourcing); Brint (discusses online communities)

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