A lot of my research can be seen as look at the effects of various types of technical interventions on social media systems. I change something about a social media system, and I look to see how people act differently. There are many different ways of trying to predict what will happen, but in this post I wanted to think through what are the possible types of outcomes; what changes can happen as a result of these interventions?
Most of the time I (and other researchers like me) look at individual-level outcomes within the site. Do individual users increase their contributions to the site? Do individual users participate more by leaving comments or rating items? Most of the outcomes I identified in a previous post fit in this category. This is the easiest category of outcomes to study, since within-site outcomes are easy to measure, and there are usually lots of individuals using the site.
A second type of outcome is group-level or system-level outcomes within the site. These are outcomes that concern group/system level properties. For example, we could ask the question “is there a sufficient number of users contributing for the site to be self-sustaining?” or “even though some users increase and others decrease their contributions, is the total quantity of information on the site increasing?” My recent paper on minimum thresholds looks at this second question. These questions are different because they look at the site as more than simply an aggregate of individuals; they assume that there exist group-level properties of interest. Often looking at these outcomes involve looking at tradeoffs across individuals, such as the “some people decrease contributions but others increase” tradeoff in the minumum threshold research. These types of outcomes are hard to study because each social media system is N=1; the system can only have a single outcome. I have obviously done some work on these types of outcomes, and I intend to do more in the future. I think economic modeling is actually a powerful tool for this because it can look at group-level properties and tradeoffs.
A third type of outcome is the individual-level spillover outcome. Often people end up changing other parts of their life as a result of their use of a social media system. The field of communications calls these “media effects.” For example, people can feel more connected and have more friends because of their Facebook use. Spillover effects don’t necessarily exist, but they can be an excellent reason to study social media systems when they do exist. However, it is generally quite difficult to study spillover effects; simply having access to data from the social media system is not sufficient to study spillover effects. You must also have data from the individual users about life outside of the social media system.
Finallly, the fourth type of outcome is a group-level spillover outcome. These are outcomes that result to larger social structures as a result of social media use by the individuals in the group. For example, a number of business have adopted various social media systems (IBM has gone full out, producing their own social networking system Beehive, their own social bookmarking system Dogear, and their own microblogging system). These businesses hope that these systems have enterprise-level spillover effects. (They also hope these effects will be generally positive for the business, like making people more productive, or encouraging innovation through collaboration.) Unfortunately, these are the hardest types of effects to measure because they have all the difficulties of both group-level effets and spillover effects. Often research into these effects are case studies that follow how one particular system has impacted a given organization.
When thinking through the various ways that social media systems can have an impact, this taxonomy of effects might be useful.
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