Stanford Talk on New Media Metrics & Analytics

[#1]

Earlier this month, I was invited to give a workshop at Stanford University. This workshop, titled New Metrics for New Media: Analytics for Social Media and Virtual Worlds, is one of many that are sponsored by Media X. It was held at Wallenberg Hall on Aug 5 and 6.

The workshop was organized by Martha Russell (Associate Director of Media X) and Marc Smith (Chief Social Scientist of Telligent), and consists of panel discussions from industry leaders, various talks, and hands-on tutorial sessions. I was very privileged to be invited by Martha Russell to give a talk on Measuring Social Media & Digital WOM. Besides giving my talk on CHI, and the mechanism and benefit of community word-of-mouth, I also learned a lot from the workshop. Since my days are still overwhelmingly busy, I've decided to write a short blog this week to share with you one of the most interesting metrics that I've learned.

Inferring User Intentions:

One of the most intriguing metric I learned from this workshop is intention: what does a user want to accomplish. Intention is not really a metric in the traditional sense, but it is something that we can potentially infer based on other metrics. Although this might sound a little scary, I believe it is beneficial. Information retrieval system could benefit greatly from understanding exactly what the user is looking for.

Although some business may leverage this information and spam their audience, but a spam to one may turn out to be useful information to another. A key for Google's success in ad targeting is that the ads are sufficiently relevant to the user that they are no longer treated as spam. The reason businesses spam today is precisely because they are unable to accurately infer user intent. If we can truly infer intention, there is no need to spam at all. In fact, spamming would become impractical and no one would do it because its efficacy is so low.

However, intention inference is not easy. The only algorithm I know that is capable of performing such inferential task is belief propagation. However, this algorithm is computationally very expensive. Even with modern cloud style supercomputing, running a large-scale belief propagation may still be infeasible. Yet, I am fascinated by this inference problem from a theoretical point of view. Much can be analyzed before one finds a practical implementation. That is why we need researchers!

OK, that is it! Told you this will be a short one. Let me know what you think about this metric. If someday we discovered an efficient algorithm for intention inference, would you be excited or afraid? How would you use this information?

Photo by Marc Smith