KM on a dollar a day

Musing on knowledge management, aid and development with limited resources

Confusing crowds with communities

with 12 comments

(Life of Brian on crowds)

This morning I attended the Social Media Week session “Open UN: The evolution of the crowd” (see programme and video of the event here on the UN Pulse website).

I was particularly interested in this panel since I’ve had a long standing interest in “the wisdom of crowds” and how this might be applied in development which predates my current work on knowledge management and communities of practice.

Listening to the panel discussion though I quickly realized that while the panel was notionally about crowds, in fact what most of the panelists were describing were the creation of communities, not crowds at all.  Mark Belinski of Digital Democracy even went as far as to say “Communities share info, crowds make noise”.

If we are to think about “listening to the crowd” and “crowdsourcing ” then it’s important to have a clearer understanding of the differences between crowds and communities where each occur and how we can “use” them for knowledge sharing for development.

A community is a group of people who come together to act around a common purpose or “intent”. It is also characterized by interactions and relationships between its members who are in some way dependent on one another. A community may have formalized organization and rules, or it may only have informal norms, it may be intentionally designed or it may “emerge” spontaneously. It may have clear leadership or it may have highly decentralized leadership. As I mentioned in a previous post – even if a community emerges spontaneously, over time it will develop its own rules, norms and heirarchies and methods of co-ordination. It’s not a flat leaderless structure where all members have equal influence.

A crowd by contrast is made up of individuals each pursuing their own interests. While information might emerge from a crowd it is the result of individual actions (and often each member of the crowd watching and reacting to what the others are doing) but it doesn’t have a conscious “purpose” or organization. An example might be the stock market where stock prices are set by the “crowd” of investors each making their own decisions pursuing their own goals, but from which information and order (in this case a stock price) emerges.

Often communities can emerge from crowds but they are not the same thing, and that matters. Here are a few of the implications of the differences.

1. People choose to be in communities. Communities  can represent “local ownership and participation” in the development process, and an opportunity for aid beneficiaries to have a voice. It also provides development workers with clear interlocateurs to interact with. It’s therefore clearly desirable to help foster communities and provide the means for them to self-organize and communicate and take action. BUT we should also recognize that communities establish rules and hierarchies even if these evolve over time and are not formal or explicit. This means that not all voices in a community are equal. Some members have more influence than others, and some make a greater contribution to the cause than others. Not all members of a group of beneficiaries will necessarily be willing or able to join a community or play an active part in it, and not all voices or interests are treated equally. Those with more personal influence and those who are able to put in most effort will also have more influence over the community and most likely benefit most from it.

2. Since  crowds are more passive or “purposeless” then you can’t interact with them as if they have a common voice. But a crowd can include members of beneficiary groups that don’t actively participate in any communities. Here the means to collect information from the crowd is also more passive, and it’s here where a broader sense of “information exhaust” can come into play. To understand a crowd you need to try to observe what a crowd does, and aggregate it into meaningful data. This can be by collecting and analyzing lots of micro level data on behaviour such as visits to health centres, mobile phone usage, school attendance etc. and then using analytical techniques to make sense of it. It can include collecti0n systems that require participation such as voting or opinion polls, or SMS alert systems such as Ushahidi – but in this case we need to be careful to ensure the barriers to participation are not so high as to bias the information collected. In a crowd situation generally one voice is not more important than another, or if it is this is not due to the communication and leadership skills of the contributor. Observing crowds can also be a better way to see divergent or minority views or situations within a population whereas communities often promote convergence around a single viewpoint.

A key difference here is that we collect data from crowds and interpret it ourselves often using analytical techniques to make sense of it, whereas in a community, the community itself determines the “meaning” of the information collected. Crowd data could be thought of as more “objective” since it is is based on observation, whereas community data is more subjective as it is based on the communities interpretation of its own situation, but it also gives a more contextualized understanding of the local situation.

But both of these approaches can be very valuable for development giving complementary views of the same situation by bringing together observation and participation. It’s not a question of communities versus crowds but rather making best use of each to better understand a situation, engage with beneficiaries and work together to take appropriate action.

Written by Ian Thorpe

February 8, 2011 at 11:37 pm

12 Responses

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  1. […] This post was mentioned on Twitter by Ian Thorpe, Chan Stroman. Chan Stroman said: Confusing crowds with communities « KM on a dollar a day: http://bit.ly/e3EIqD […]

  2. Very good article! Thank you. I know the difference between communities and crowds now. But since you said that it is not so much about the difference, but to apply them in the right situation, what do you think would be a good situation to:
    (1) cultivate communities of practice
    (2) apply wisdom of the crowds

    Roan Yong

    February 9, 2011 at 9:03 am

    • Hi Roan

      In brief – fostering communities are a good approach where you want to engage a group of people around a common issue (might be customers around a product or beneficiaries around a service or problem to be addressed). There needs to be a potential group to forma community and a common issue around which they can communicate and collaborate.

      Crowd data can be used even where there is not a group with a common interest, or where you alswo want to collect data from those people who are unable or unwilling to play an active role in a community.

      Both approaches can be used side by side, for example you could engage a community to provide feedback on health needs and management of health services. At the same time you can also collect crowd data about illness and use of health systems/services. Both of these information sources can be complementary and help you (and the community) plan better more responsive health services.

      Ian Thorpe

      February 9, 2011 at 11:36 am

  3. Excellent post. I just wrote a blog on pros and cons of crowdfunding in philanthropy and I think your distinction between crowds and community puts a much finer point on it. http://www.crystalhayling.com. Thanks and keep up the good posts.

    Crystal Hayling

    February 9, 2011 at 9:31 am

  4. Very helpful clarification of what has become a confusing use of terms…I especially like your choice of a video clip to illustrate!

    Bonnie Koenig

    February 9, 2011 at 9:57 am

  5. Hi Ian, very good that you are writing this down- crowdsourcing is a very fancy, hyped word with all the confusion that comes with it!

    Joitske Hulsebosch

    February 9, 2011 at 10:45 am

  6. […] more here: Confusing crowds with communities AKPC_IDS += […]

  7. […] blog post from Ian Thorpe, KM on a Dollar a Day: Confusing crowds with communities, helps sort out the distinction.  It also includes a great video clip from Monty Python’s Life […]

  8. […] this topic including :  The Real Active Value of Community Management by Debra Askanase and Confusing Crowds with Communities by Ian […]

  9. this is very interesting for my line of work (evaluation) because I think what we normally call a “focus group discussion” is essentially a small crowd. You choose a few people that are representative to you, (or what you are trying to look at), but who don’t necessarily have anything in common, and generally represent their own interests (or of their own organization) that are not necessarily in line with the rest

    angelica

    February 10, 2011 at 1:22 pm

  10. Hi, Ian,
    very valuable to see the 2 phenomena distinguished, your words are clarifying. So I am very positive about your post.
    As mentioned earlier, I have problems to understand the terms “objective” and “subjective” as you mention them.
    It might not conclusive as well, I tend to think that crowds provide more data / information, while it is the communities that work to create knowledge by putting the data / information into context.

    regards
    gerald

    Gerald Meinert

    February 16, 2011 at 12:17 pm

    • Gerald – thanks for the comment.

      What I meant by crowd data being “objective” is that it is observable and replicable. As you say, it is data, data aggregated from observation using an algorithm.

      Community outputs could be considered knowledge – but importantly they are based on the collective interpretation of the group. as such they are not neutral since they depend on the interactions and power dynamics within the group. They are not just interpretation of the data since in some cases the output (opinion) of a community might be contradicted by the observable data.

      That said – it doesn’t mean that community outputs are less useful or reliable than crowd data. Just that they are qualitatively different both in how they collected, but also how you use them.

      Ian Thorpe

      February 16, 2011 at 9:19 pm


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