Model-Driven Research in Social Computing

I'm in Toronto attending the Hypertext 2010 conference, where I gave the keynote talk at the First Workshop on Modeling Social Media yesterday. I want to document a little bit of the points I made in the talk here.

The reason we seek to construct and derive models is to predict and explain what might be happening in social computing systems. For social media, we seek to understand how these systems evolve over time. Constructing these models should also enable us to generate new ideas and systems.

As an example, many have proposed a theory of influentials that identifying a small group of individuals who are connected to the larger social network just in the right way, we can infect or reach the rest of the people in the network. This idea is probably most well-known in the press by the popular book Tipping Point by Gladwell. This model of how information diffuse in social networks is very attractive, not just due to its simplicity, but also the potential of applying this idea in areas such as marketing.

Models such as this are meant to be challenged and debated. They are always strawman proposals. Duncan Watts' simulation on networks have shown that the validity of this theory is somewhat suspect. Indeed, recently, Eric Sun and Cameron Marlow's work, published in ICWSM2009, showed that this theory of influentials might be wrong. They suggest that "diffusion chains are typically started by a substantial number of users. Large clusters emerge when hundreds or even thousands of short diffusion chains merge together."

Most, if not all, models are wrong. Some models are just more wrong than others. But models still serve important roles. They might be divided into several categories:

1. Descriptive Models describe what is going on within the data. This might help us spot trends, such as the growth of number of contributors, or trending topics in a community.
2. Explanatory Models help us explain what might be the mechanisms underlying processes in the system. For example, we might be able to explain why certain groups of people contribute more content than another group.
3. Predictive Models help us engineer systems by predicting what users and groups might want, or how they might act in systems. Here we might build probabilistic models of whether a user will use a particular tag on a particular item in a social tagging system.
4. Prescriptive Models are set of design rules or a process that helps practitioners generate useful or practical systems. For example, Yahoo's Social Design Patterns Library on Reputation is a very good example of a prescriptive model.
5. "Generative Models" actually have two meanings depending on who you're talking to. In statistical circles, "generative models" are models that help generate data that look like real user data and are often probabilistic models. Information Theory is a good example of this approach, in fact. Generative Models could also mean that they are models that help us generate ideas, novel techniques and systems. My work with Brynn Evans on building a social search model is an example of this approach.
   

In the talk, I illustrated how we have modeled the dynamics in the popular social bookmarking system, Delicious, using Information Theory. I also showed how using equations from Evolutionary Dynamics we were better able to explain what might be happening to Wikipedia’s contribution patterns. Talk Title: Model-driven Research for Augmenting Social Cognition

Model-Driven Research in Social Computing

View more presentations from Ed Chi.

Ushahidi: A crowdsourcing site you probably have not heard of

Us research scientists always go after the latest and greatest shiny cool thing to study on the Web (like us with Wikipedia), but of course, the real world is full of chaos, anger, fear, and all the unpleasant things we all prefer to forget about. What can the Social Web, Collective Intelligence, and Utopia have possibly anything to do with all that?



Ushahidi is a "platform that allows anyone to gather distributed data via SMS, email, or the web, and visualize the data on a map or timeline." The goal is to "create the simplest way of aggregating information from the public for use in crisis response." In March, while I was on an around-the-world trip to Beijing and Amsterdam, I read this article in the NYTimes about Ushahidi, and thought about how work like this reaffirms my belief that the Social Web is changing how information is distributed and used in the world, and that it is revolutionary. Ushahidi (which means testimony in Swahili) has now been used in the Kenya's disputed election in 2007 (documenting the violence) as well as Haitian and Chilean earthquakes. "It collected more testimony with greater rapidity than any reporter or election monitor." "The site collected user-generated cellphone reports of riots, stranded refugees, rapes and deaths and plotted them on a map, using the locations given by informants."

Wow!

Let's think for a second about what happened. Someone (Ory Okolloh) who cared about what's happening in Kenya blogged about what was happening, and thought about how a web application could change the transparency of the events to be visible to the world, and then tech geeks read her post and build the web site over a long weekend. Then boom! The world changes.

Shocking.

Why did it work? What's the participation architecture? And what role did technology play in this? Clearly, attention around an event was aggregated, and this came as a result of a confluence of events. The participation architecture relied on the fact that people cared enough about what's happening to build the system, and the people on the ground to have the right technology to report the events to the website, and technology enabled the mapping of these events to a map. Viola! Mass data visualization results.

Amazing.

Amazing because this happened in such a distributed fashion. No government agency got involved, and no centralized authority coordinated the work over a multi-year government grant. Now this has been exported back to the USA and in Washington D.C., the system was used to warn about dangerous roads during the big snowstorm.


The same snow storm that caused the Technology mediated Social Participation workshop to move the date of the 2nd East Coast Workshop. Ironic, isn't it?

Ironic also because this was almost precisely what I had proposed to a Gov't funding agency program manager who visited PARC about 3 years ago (Aug 2007) who was interested in disaster response. She never followed up and we weren't funded on the idea. But here are a few slides from that presentation:

ASC Proposal for Disaster Response Research in Aug. 2007

View more presentations from Ed Chi.

I think Ushahidi is awesome. Ushahidi happened because of people believed and cared about what is happening in the world. That, to me, is the power of the social web.