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Online Participation, Crowdsourcing, & Communities; part 1

21 Feb

This week’s class will focus on the impact of crowds and communities. I cannot say for sure, but I am almost positive that there is a wide range of compelling data (not just from the first review) that dictates crowds can accumulate knowledge in a more robust and pleasing fashion than even the smartest person. Understanding distributions and balance greatly eases the understanding of these readings, however I will attempt review them in such a fashion that a simplified explanation can suffice.

Wisdom of Crowds

The first article is particularly compelling as it defines the beginnings of statistical analysis in regards to the bell curve. I should make mention here that I am not just enrolled in TECH621: Social Internet, but also in CGT600: Spatial Ability Research. Upon seeing Galton at the forefront, I immediately knew that this would be an article discussing the basis of cognitive ability and reasoning. That is-the article is very rich in discerning how people operate based on available knowledge. Many people have a tendency to go with a solution that works for now, rather than the best-case answer. Amongst this population, we have individuals who try to base decisions on their immediate involvement, or an emotion attached to the scenario. The knowledge of this one person is…lacking, to say the least. However, the collection of knowledge from individuals across a population (be it smart, silly, serious, or otherwise) reflect an intelligence that the individual would have a difficult time obtaining solo. So, regardless of how high your IQ test is, the crowd still (most likely) knows better than you.

The behaviors of crowds can happen on purpose: a good example is the Obama committee’s efforts to gather more young people in the poles, which most are aware was an AMAZING success-or subconsciously; I think a good example here is the boat scene from the Dark Knight, where everyone on two separate boats had to make a decision as to whether or not to blow up the other boat. By killing off one, the Joker would leave the other be. However, if no one decided, he would kill them both. Despite the implications, both boats had individuals pro-other boat’s destruction, and anti. The final decision was made by one person; however, it still reflected the overall consensus: amongst the boat of prisoners, a man threw the remote out the window and said “I’m going to do what you should have done 3 minutes ago.”; the other boat consisted of hard-working commoners, who, despite their reasoning of the other boat ‘housing men who had their chance’, ‘holding men who will die anyway’ the morality of one carried deep through the fight-he simply could not do it, even after a long discussion of how he’ll do it since no one else could. The point: the view of the crowd is what made the final decision, not the person. The example also happens to touch on the three kinds of problems the author mentions: cognition, cooperative, and coordination problems. Cognition: “What happens if we don’t make a decision?”; coordination: making the decision as a group; cooperative: competing with self-interest value conflicts. It also touches on how the best decisions are bred from disagreement and contest, versus consensus or compromise. It’s easier to get people who agree with you to follow your lead than those who can present an opposing viewpoint; it is this very reason (and definition of diversity) that the clash is more effective: it forces you to think outside the box, try to understand *why* the person disagrees with you. Take note, politicians! It continues with examples and the following four points that constitute a ‘wise crowd’: diversity of opinion, independence, decentralization, and aggregation.

Light and Heavyweight Models of Peer Production

This article provides an overview of how populations amass data. Lightweight models are used with implications of obtaining knowledge from anyone. The information and data that is obtained in a lightweight model can be obtained from a very generalized population-assuming that there are rules, regulations, and some form of ‘funneling’ information active. Contributions in this model are overall the same, due to the fact that an outside source (such as an administrator) defines what will be contributed. Heavyweight peer productions allow for mass collaboration with no restrictions. Through this collaboration, methods are refined, publications are revised, and information is more widely distributed. A decision is made only after enough data from the population is acquired, not when the mediator says he/she/it has enough of the same thing to find significance. The article uses Mozilla bug fixes as a LWPP example, the academic community as a HWPP, and Wikipedia as dual weight (the information is monitored/regulated, but through the very population that contributes it). LW tends to be more individual (how does this affect/pertain to you, specifically?), while HW is more group-oriented (why do you feel this needs a change, and what change are you proposing?). Each model carries a sense of recognition, reputation, and reward: in LW it is mostly a quantitative analysis, while HW is more qualitative, and the three are based on what peers have to say and contribute about your work. It continues discussion of weak ties v. strong ties (respectively to models),and how these ties play large roles in defining LW and HWPPs.

Social Network Sites as Virtual Communities

A need to understand what constitutes a community becomes relevant in the descriptions of both virtual communities and social networks. The term ‘community’ is debated between theorists as ‘quality of sociality’, allowing it to be applied across many forms of interaction and communication; prescriptivists define it as ‘groups of people who share physical space, are sufficient in that space, and who share ties that include kinship.’ It later reveals five recurrent themes that serve as a basic criterion in which groups function as a community:

  • the ability to engage in collective action
  • the group thinks of itself as a community
  • members identify with the community
  • information is shared
  • large patterns of interaction grow through regulized information exchange

A final important point is brought up defining community: members exhibit attachments to one another and to the community more generally. It then discusses social affordances-the possibilities for action called upon by a social technology or environment. Topics of this article’s interest are affordances of membership (criteria for being able to ‘join’), expression (customization), and connection (groups, message, and communicative means). This article strives to figure out just how these social affordances are used, how often, and what encourages the formation of communities. The test conducted is using MySpace as a testing base, and it was found that, while the social affordances were being used, it was not to such an extent that it constituted a virtual community.

-Vanessa B!

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1 Comment

Posted by on February 21, 2011 in Uncategorized

 

One response to “Online Participation, Crowdsourcing, & Communities; part 1

  1. Mihaela

    February 22, 2011 at 12:12 pm

    Good job posting your reading notes – thank you for posting! There are a couple of points that need better clarification, so pay attention to them in class: one is the definition of cognition problems; the other one is models of peer production, rather than “how crowds amass data.”

     

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