Pondering Online Communities and Fluid Social Groups

A friend once told me that if I were a superhero I would be called “The Includer.” She was right, I’m usually the one trying to get more people involved in whatever is about to happen. Superhero or not, my crowd-mongering has taught me one thing: Groups are complicated.

I’m sure you know what I mean. Sometimes people only feel like hanging out with the “core.” Or maybe someone has decided that they like the group, but can’t stand a few of its members which causes a rift. The dynamics of even a small group can drastically shift with a single addition or removal. More often than not, butting out and letting things unfold on their own is the only safe bet.

In my experience, there is no such thing as a “one size fits all” group, even among friends. The ideal shape and nature of a group changes depending on the context and whose perspective you are considering. If we want social media to evolve into a more meaningful form of dynamic group interaction, fluidly defined communities need be seriously thought through.

Let the Pondering Begin

A community doesn’t have to be social to have personalized borders — physical communities and informational communities have their own unique flavors of fluidity. The more we can understand and reflect individual views of the interactions we host, the more relevant and engaging our systems will be.

• Location-Based Communities

  Geopolitical regions are nice, but not all locative information fits this kind of organizational model. Unless we’re talking about government or a large-scale event, I’m far more likely to care about the happenings, thoughts, and commentary of my block and immediate neighborhood than the collective buzz of my entire city.

  I’m guessing the same could be said for my neighbor, and my neighbor’s neighbor, and so on — at some point that path ends up outside of my “care zone.” Each person along the way has a slightly different circle of interest. A lot of those circles overlap with mine, and that overlap defines a fluid physical community — my neighborhood.

• Knowledge-Based Communities

  As a programmer, there is no question that I learn from my peers. I grow by discovering the knowledge that PHP developers, SQL gurus, and other specialized members of the traditional “developer community” have to share. At the same time, however, I benefit hugely from interactions with journalists, designers, managers, entrepreneurs, and academics. Together, these people define a personal knowledge community just for me.

  Knowledge-based communities often surround a common interest, but they go a lot deeper than that. A person’s ideal information pool needs to hone in on their specific interests, but should also incorporate the more elusive “second degree” interests (e.g. technology and entrepreneurship; snowboarding and meteorology) in the name of breadth.

• Network-Based Communities

  There is a field of Mathematics called graph theory. Despite the name, graph theory has nothing to do with the charts you learned in high school. In this case, graphs are “mathematical objects” which, believe it or not, are pretty simple (think connect the dots from back in kindergarten). They are used to represent relationships between objects through “edges” (lines) and “vertices” (dots). Graphs are a great way to represent groups: the group members are vertices and the relationships between those members are edges.

  Below is an example of a small graph which represents one person’s group-based community (Person A). Maybe it depicts person A’s best buds, where the edges represent strong friendships, or maybe it’s a map of his workplace where the edges represent people who often talk to one another.

  

  Although everybody involved shares a common group identity, the graph tells a completely different story depending on whose eyes you look through. For instance, person C tends to hang out with A and B. Meanwhile A, D, E, and F form a tight knit group, but A and F also share a close mutual friend.

  In the real world, these graphs would be far larger, far more complex, and would vary far more from person to person. Suddenly Facebook and Twitter’s tendency to bypass community and stick to networks makes a lot more sense.

The Tale of Clipt

Last year my friend Erek Alper and I submitted an application to the Knight News Challenge for a project called Clipt. The idea was a “digital conversation platform that hosts discussions grounded in bite-sized ‘clips’ of audio, video, image, and text that have been captured online.”

Clips and posts would be submitted by users, tagged to locations and topics, and linked to each other. This content would then be displayed as interlinked bubbles in scalable, fluid interfaces called “clipboards” (see below for one of our early mockups to get a basic visual). These user-defined views of the system’s global content base were created in terms of:

• Location: Adding a location to a clipboard was simply a matter of placing a pin in a map. The clipboard then contained content associated with an area around the pin, and more proximal content (thoughts and clips created by locals or tagged to a location) would get more exposure.

• Topic: Setting topics to a clipboard would narrow down what content was displayed. As the user added topics, the clipboard would become more and more relevant to a specific set of interests.

• Group: Users would be able to define a clipboard so that it would only show conversations involving specific people. Conversations involving more than one of those people would be more likely to appear on the board.

A piece of content that met all of the clipboard’s conditions wouldn’t necessarily be displayed if there were way too many matches. Instead, the interface allowed users to move along the “relevance gradient” — zooming in to increase the focus (which would do things like “increase the required number of conversing users,” or “decrease the physical radius of interest”), and zooming out to get see more general content.

Clipboards and their ability to capture relevance were our answer to the “fluid group” problem. The hope was that by defining filters for clips and thoughts in terms of location, topic, and group, our users would be able to follow and create tapestries of community conversation without resorting to rigid categorizations and boundaries.

Despite Knight’s rejection, I still think the idea has great potential. That being said, I realize there are plenty of other ways to achieve personalized community. If you have any bright ideas of your own, please share a musing below!