ICWSM Session 5: Panel Discussion – System Design and Community Culture

System Design and Community Culture

The role of rules and algorithms in shaping human behavior


  • Lukas Biewald, Dolores Labs
  • Rashimi Sinha, Slideshare
  • Cameron Marlow, Facebook

Dolores Labs – Making Crowds Efficient and Reliable.  They pay people to perform tasks, aka Amazon Mechanical Turk.

Slideshare – Focus on social design.  Presentations are fundamentally social – you don’t make them for yourself.  The social networking tools (commenting, favoriting, tagging) has lead to the creation of a community.

Facebook – Runs the Data Science team, which uses machine language and research to understand how users use the site, and that leads to design changes.

Examples of Unexpected Community Behavior?

RS: What gets spam, what does not.  Particularly in their comment system.  They went through lots of iterations

LB: Prompting a task affected the outcome.  So now they work with people to define

What sort design decisions are based on difficulty?

LB: Try to break a task into the smallest possible unit.

RS: Presentations are less frequent than say photos, so there are different rules.  Also differentiate between user types: content creator, readers, aggregators.

CM: Facebook isn’t really designed around a task.  They do lots of things to enable use at different levels.

Range of tasks across the three systems.  How do you learn how social interactions change tasks?

RS: Observed real life events (people gather around a presentation). Create a unit, and a construct around that.

CM: FB tries to lower the barrier of trying now tasks.  For example, someone can upload a photo, others can tag photos, add metadata, etc.

Design by Intuition vs. Design by Data.  What is your approach/process in developing new features?

RS: Start with intuition, primary hypothesis.  Look at what data in the world.  Once its up, there’s lots of data to see what people like, what people talk about.  Also do AB Testing.

LB: Can nicely segment users along whatever dimension you want, so you have lots of options.

CM: People react to change.  Some like it, some hate it.  What fraction of the population respond to the change.

We know you can prompt people to get certain types of behavior.  How do you compensate for that?

RS: Not so worried about that — doesn’t have to be scientific.  Of course, you can also do experiments to deal with it.

CM: There are many sources of bias in these large ecosystems.  Important that decision makers know about them.

Community, communicate, share.  What makes for successful conversation?

CM: Allow them to happen at a different scale, use aggregated tools to understand entire conversation.  For example, they have a tool that can find a term/keyword across all of Facebook, as a percentage of all text.  Helps them make sense (in some small way) of everything.

RS: Twitter hashtags are a really good, scalable way to communicate a topic.  Well, maybe partially scalable.  When a hashtag makes twitter trending topics, bots take over.  But things are good up until then.

How do people discover your content, features?

RS: Email, social network links, but mainly Google search

CM: The Wall.  Now have two feeds: 1 real time, 1 algorithm driven.

Twitter innovations: #hashtags, @replies, ReTweets – users came up with those.  How do you design so that users can extend the design on their own?

RS: Initial version of Slideshare was barebones.  Keep the initial design to the core, get feedback, refine.  Build new features based on what works.  Also, develop and API so people can extend your site.

CM: Design a platform so that people can build their own specific tools.

How do you enable the conversation/feedback between designers and community?  How do you differentiate edge case complaints vs real problems.

LB: Designers do customer support

RS: Ditto.  Also, use numbers, percentages of people that complain.

CM: Collect as many signals as possible.  If something shows up across many areas, it may be a real problem.