Category Archives: Social computing

Motivating participation in social networks

It’s not immediately obvious that there is too little participation in social networking sites like FaceBook and MySpace. But providers of such services, and providers of ancillary services, would certainly like to see more participation. And if they can find commercial value in stimulating more activity by willing users, then the economist in me concludes we’re better off: no obvious reason why the marketplace would be failing here.
At the least, social welfare musings aside, providers clearly want to motivate more participation, and more loyalty (that is, convincing folks to keep participating, rather than moving on to new networks). How to do so?
urTurn, an Ann Arbor startup, hopes it can create a market for measuring, monetizing and motivating more social network participation. It’s idea is simple, and may work: if there’s commercial value to providers in having lots of active users, then share some of that value back to the users. By letting the users benefit not only from the social activities, but also from a share of the commercial value of those activities, they should be motivated to participate more.
urTurn is trying to do this by creating a new currency. They award members points for activity, similar to a frequent flier program. The points can then be converted in various ways into cash or swag. For example, urTurn maintains a marketplace where users can buy and sell points for real cash (in trades with other users), much the way that people actively exchange Linden dollars for US dollars in the virtual world, Second Life. In addition, urTurn offers a store for cashing in prizes: for example, for 7500 points you can buy a $25 Visa cash card (other items for sale include iPods and iPhones). Soon urTurn will also be offering auctions in which users can bid their points to obtain items.
Here are some more details, written by my friend and colleague Prof. Yan Chen. Yan has no financial interest in urTurn (she is studying it for some of her research, and is helping to design the auctions for them in exchange for access to data).

With the launch of Google OpenSocial and its embrace by MySpace, etc.., the ability to cross social networks with a common currency is now readily available. The urTurn Rewards Widget monitors and tracks specific activities on the host social networks – Facebook, MySpace, bebo – and gathers these in a central account on the urTurn Marketplace – activities such as making friends, posting photos, and linking your widget to your urTurn Marketplace account.
Users on social networking sites, can download the widget, and start earning points for certain activities, including subscribing to the widget, forwarding the widget to a friend, friend’s subscription to the widget, posting a blog or photo, adding friends to Facebook, and status update on Facebook.
In contrast to frequent flyer programs, urTurn does not benefit directly from the points it rewards the users of host sites. Its revenue might come from several sources: ads revenue, taxing the transactions on the points market, and possibly the host sites.

To add the urTurn application to your Facebook page, go to the URL:
http://www.facebook.com/apps/application.php?id=17299885707&ref=s

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ICD for home computer security

Ph.D. student Rick Wash and I are applying ICD design tools to the problem of home computer security. Metromode (online magazine) recently published an article featuring our project.

One of the major threats to home computers are viruses that install bots, creating botnets. These bots are code that use the computer’s resources to perform something on behalf of the bot owner. Most commonly, the bots become spam sending engines, so that spammers can send mail from thousands of home computers, making it harder to block the spam by originating IP (and also saving them the cost of buying and maintaining a server farm). Bots, of course, may also log keystrokes and try to capture bank passwords and credit card numbers.
The problem is crawling with incentives issues. Unlike first generation viruses, bots tend to be smarter about detection. In particular, they watch the process table, and limit themselves to using CPU cycles when other programs are not using many. That way, a normal home user may not see any evidence that he or she has a virus: the computer does not seem to noticeably slow down (but while they are away from the machine the bot may be running full tilt sending out spam). So, the bot doesn’t harm its host much, but it harms others (spreading spam, the bot virus itself, possibly other harmful activity like denial-of-service attacks on other hosts). This is a classic negative externality: the computer owner has little incentive (and often little appropriate knowledge) to stop the bot, but others suffer. How to get the home computer user to protect his or her machine better?
We are developing a social firewall that integrates with standard personal firewall services to provide the user additional benefits (motivating them to use the service), while simultaneously providing improved security information to the firewalls employed by other users.
We don’t have any papers released on this new system yet, but for some of the foundational ideas, see “Incentive-Centered Design for Information Security“, ICEC-07.

The social psychology of Facebook?

John Kirriemuir wrote a casual entry in his blog about the “psychology of Facebook”. It is a lighthearted piece, but thoughtful. He suggests various informal hypotheses about why they spoke is succeeding, focusing in particular on the effort people make to grow their networks.
I would like to start learning about social psychology theory and what it might usefully say for incentive-centered design of information systems. My expertise in ICD is largely grounded in individual utility maximization and game theory. I have been saying for the last couple of years that “social motivations” are clearly important for some of the fundamental issues (motivating people to contribute to public resources, motivating them to make effort sufficient to generate high-quality contributions, and motivating them not to misuse and open access platform for unintended purposes). But other than my instincts and casual observation, I have little to go on.
Kirriemuir is not a social scientist (and is clear that he is not claiming to be), and his article is also casual. The social motivations he suggests are not clearly enough to find to test them or generalize to other settings, and his analysis is ex post description, which really does not serve as explanation (in the sense of enabling us to predict or successfully designed in other settings). But he asks good questions, and I think he is right that humans respond to various predictable social motivations in ways that are important for the success or failure of different social information systems.

Incentives for bookmarking

My Ph.D. student, Rick Wash, together with Emilee Rader, has a new paper on incentives for bookmarking in del.icio.us. This paper will be appearing, after some revision, in ASIST 2007, as “Public Bookmarks and Private Benefits: An Analysis of Incentives in Social Computing“.
In this study, based on in-depth field interviews of del.icio.us users, they conclude that

metadata reflecting who bookmarked a webpage better supports information seeking than free-form keyword metadata (tags). We explain this finding by describing differences in the way that the design of del.icio.us motivates users to contribute by providing personal benefits for bookmarking and tagging.

Incentives and tagging (Library Thing vs. Amazon)

Rick Wash pointed me to an interesting blog article about a comparison of book tagging on LibraryThing and on Amazon. The basic fact asserted: tagging is wildly successful on LibraryThing, and has barely had any meaningful usage on Amazon.
The more interesting point for us: why? The author suggests that the incentives are aligned much better at LibraryThing. At some level, that’s tautologically true, but what we might learn from is what the incentives are.
The rather obvious, but important point the author makes (but here in the pithier words of one of the commenters on the post):

people do stuff on the Internet that is useful to them, not out of the desire to make a nifty tagsonomy.

The result may be that a very valuable public good is created (which is true at LibraryThing), but it usually created because the individuals contributing were getting enough value for themselves. This is the compelling logic behind the private provision of public goods.
On LibraryThing, people are cataloguing their own book collections, for their own purpose. Tagging creates organization, that can be used for sorting, reporting, finding. This same motivation is at work on flickr (photos) and del.icio.us (bookmarks).
On Amazon, people are searching to buy books they haven’t read: what gain to them from tagging them? (Some suggest tags can be used to create complex categorized wish lists, but how much value do they add to the flat wish list, when few people realistically keep more than a dozen or two items on their wish list (and tagged structures are not easily viewable by potential gift givers).)
Of course, as striking as this example is, and apparently compelling the logic, it is not so easy to explain all user-contributed content. One obvious relevant example: Why are people spending so much time writing book reviews on Amazon? Surely not primarily to create a set of notes to jog their memory later about what they thought about a book?

Getting good stuff in: Participation Inequality

An interesting phenomenon, noted by many, is that most content in user-contributed content venues (including online communities that focus more on “community” than on creating a durable information resource) is provided by a small fraction of users. Many have documented that participation in a wide variety of voluntary settings follows a power law (that the amount of contribution decreases proportional to 1 over the rank of the contributor).
Jakob Nielsen offers a nice summary including some historical references:

In most online communities, 90% of users are lurkers who never contribute, 9% of users contribute a little, and 1% of users account for almost all the action.

This sort of participation inequality has been seen in online communities, Amazon book reviews, Wikipedia edits, blogs, and peer-to-peer file sharing networks, to name a few venues (for the latter, see E. Adar and B. Huberman, “Free Riding on Gnutella”, First Monday, 5 (2000), and S. Sariou, P. Gummadi, and S. Gribble, “A measurement study of peer-to-peer file sharing
systems”, Multimedia Computing and Networking (January 2002).)
Inequality isn’t directly an issue of “getting good stuff in”, as much as about getting stuff in at all. But of course, the quality of contributions is going to depend on who is motivated to contribute, not just how many contributors there are. Thus, the problem of getting critical mass for a user-contributed content service is not just getting enough contributors, but getting the right contributors.