Social Media is Based on Statistical Principles

April 23rd, 2008 | Categories: networks, social media, trends, wikis

The basis of social media revolves around user participation and crowdsourcing. The ultimate goal is to provide the best quality content via a combination of user-generated content (UGC) and a filtering process. In theory, the larger the user base, the more likely the best content gets highlighted.

In statistics, sample size is very important. If a given sample is too small, inaccurate conclusions and findings result. The same goes for social media. It is unlikely that accuracy and quality can be achieved with a small user base. This all leads back to the Network Effect.

When Wikipedia first arrived on the scene, critics were quick to discredit the “social experiment”. They didn’t think it could work, citing a lack of compensation for contributors, as well as possible inaccuracies. Despite all this, the project succeeded in a big way and revolutionized the social web. This paved the way for new entrants looking to capitalize on the social media trend. Digg and Last.fm immediately come to mind.

Essentially, social media is based on the Network Effect. The two go hand-in-hand. The Network Effect, in turn, relies on sheer numbers. In other words, the success of a service with a dependence on user-contribution can be strongly correlated to the size and quality of its user base.

One Comment

  1. Michael Artemiw Says:

    Aidan,

    You raise an interesting topic. Looking at a particular service or community it seems the value is likely proportional to the quantity of the content X the quality of the filter.

    How might this theory predict the mass exodus of a certain system or the adoption of the next new thing?

    What if we think of users of a service dependent on user-contribution as investors. They are investing their time in the service. They do this because they reasonably expect that this will pay dividends in the future.
    If a new service comes a long with little content, and few users, but cool features, they may be compelled to jump ship because they expect that future returns will be better.

    I believe the correlation you propose is strong. Additionally, I believe the success of a service, based on user-contribution, is dependent on the users’ perception of future benefits.

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