Your Friends Are More Interesting Than You On Average

The Friendship Paradox

Feld’s friendship paradox states that ‘your friends have more friends than you, on average’. This paradox arises because extremely popular people, despite being rare, are overrepresented when averaging over friends.

Using a sample of the Twitter firehose, we confirm that the friendship paradox holds for >98% of Twitter users. Because of the directed nature of the follower graph on Twitter, we are further able to confirm more detailed forms of the friendship paradox: everyone you follow or who follows you has more friends and followers than you.This is likely caused by a correlation we demonstrate between Twitter activity, number of friends, and number of followers.

But wait, there’s more..

In addition, we discover two new paradoxes: the virality paradox that states ‘your friends receive more viral content than you, on average’, and the activity paradox, which states ‘your friends are more active than you, on average’. The latter paradox is important in regulating online communication. It may result in users having difficulty maintaining optimal incoming information rates, because following additional users causes the volume of incoming tweets to increase super-linearly. (And this also may relate to why in large complex communities personalized moderation works better than community moderation, as explored in my last blog post).

While users may compensate for increased information flow by increasing their own activity, users become information overloaded when they receive more information than they are able or willing to process. We compare the average size of cascades that are sent and received by overloaded and underloaded users. And we show that overloaded users post and receive larger cascades and they are poor detector of small cascades.

What are the dangers of overload?

Those users who become overloaded, measured by receiving far more incoming messages than they send out, are contending with more tweets than they can handle. Controlling for activity, they are more likely to participate in viral cascades, likely due to receiving the popular cascades multiple times. Any individual tweet’s visibility is greatly diluted for overloaded users, because overloaded users receive so many more tweets than they can handle. Because of the connection between cognitive load and managing information overload, the present results suggest that users will dynamically adjust their social network to maintain some optimal individual level of information flux. (What does this mean for Facebook’s growth?)

Friendship Paradox Redux: Your Friends Are More Interesting Than You – Nathan O. Hodas, Farshad Kooti, Kristina Lerman (PDF of the paper)
http://arxiv.org/abs/1304.3480

How to design large complex online communities using social science

Sorry if I jump around a bit in this blog post but by reading these points, and listening to the video, you’ll have a better idea of how social science can help you design a successful community, using a specific kind of moderation approach. Or at least how to impress to use the difference between a theory vs design-type approach to community building to respond better to new customer needs.

OK, I am paraphrasing here so bear with me, with me taking notes from Robert Kraut’s Stanford presentation above. My aim is to show how social science can inform good online community design. So the first point is that Kraut makes that I want to highlight is that real community design is “highly multidimensional”. And that this is at odds with logic of social science which seeks to understand effects of one variable at a time, while all other variables are else held constant, to discover causality. OK, so that’s some of the fundamentals sorted. Skip to this section on the video to hear the explanation.

This social science approach is at odds with (i.e. online community) design where you are trying to figure out the configuration of all possible variables to have the effect that you want to have. Kraut says that basically with design you don’t want one variable at a time you want ‘kitchen sink experiments which are theory-based experiments which you want to try out in a relatively cheap way.

But they use agent based modelling – allow theory to be tested as models in community environment, change member behaviour, which change environment (see 1:12:56) – where the ‘Identity Benefit’ is greater when agent’s interests are similar to group interests:

Here’s how to simply capture that ‘Identity Benefit’:
# viewed messages that match // # viewed messages

In comparison for the other principal type of community benefit to members Kraut identifies, the ‘Bond-based benefit’ is greater when there is repeated interaction. Kind of obvious I guess, but this is social science, so still worth stating!

Agent-based modelling and simulated communities results

And from simulated communities what Kraut found is that the simulated agent models (taking the place of community members) produced results very similar to that observed in real Usenet groups.

So the next step is that if we have a working agent model that shows how community works we can test out different types of moderation techniques, which can test in this simulated community.

From this Kraut found that ‘Personalised moderation’ out performs ‘Community level moderation’, though this really matters significantly when dealing with a large volume of content, or diverse content. In other words ‘Personalised moderation’ works well with large complex communities.

personalised-moderation

And as an example, I see this personalised moderation functionality  appears to be available in community platform Telligent’s latest version of their analytics, which sounds useful. Be good to know which other major community platforms like Lithium offer such beneficial functionality, and how well it really works in the day-to-day:

Your community can now offer its participants dynamic and personalized recommendations of both people and content. Telligent Analytics looks at your community’s data, compares it with each member’s unique interests, and then delivers personalized recommendations to that member. Telligent Analytics doesn’t just tell you how your community’s doing; it applies the analytics to improve your community members’ experience.

So if you want to go into this study applied in more practical detail here’s Robert Kraut’s paper (pdf) with the graphs and stats:

A Simulation for Designing Online Community: Member Motivation, Contribution, and Discussion Moderation – (pdf: 10.1.1.141.6657)

Or maybe you’d like to read the chapter’s of Kraut’s 2012 bookBuilding successful online communities: Evidence-based social design:

  • Resnick, P. & Kraut, R. Introduction [PDF]
  • Kraut, R. E. & Resnick, P. Encouraging contributions to online communities [PDF]
  • Ren, Y, Kraut, R. E. & Kiesler, S. Encouraging commitment in online communities [PDF]
  • Kraut, R. E., Burke, M. & Riedl, J. Dealing with newcomers [PDF]
  • Kiesler, S, Kittur, A., Kraut, R., & Resnick, P. Regulating behavior in online communities [PDF]
  • Resnick, P, Konstan, J & Chen, Y. Starting a community. [PDF]