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

You might know from my short bio that I am an alumnus of the Computational Science Graduate Fellowship (CSGF) program sponsored by the US Department of Energy (DOE). Last week, the CSGF fellows and alumni in the Bay Area were invited for a tour of the Lawrence Livermore National Lab (LLNL). We visited the National Ignition Facility (NIF) and the Terascale Simulation Facility (TSF), and it was eye opening to learn about the big science and the challenging research that were conducted at LLNL. After the day tours, we were invited to a wine tasting and dinner at the Wente Vineyards. Although this has nothing to do with community or analytics, it was a full day of inspiration for me. So let me share some of my excitement with you.

LLNL is a classified national lab, so I had to send in my personal information for a background check weeks ahead to obtain the proper security clearance. The lab is so secured that no cell phones, cameras, video cameras, or any electronics were allowed beyond the guarded fenced surrounding the facility. Basically, after I picked up my visitor badge outside the fence, I become completely isolated from the outside world. I'd like to draw your attention to the blue casing hanging above the badge. Any guesses on what that might be? It is a radiation sensor. Although I had one of these badges when I was at the Los Alamos National Lab in 2003, I never had one with a radiation sensor, because I was working in the Machine Learning Group under CCS-3 Division, which is safely shielded far away from all radioactive materials.

Alright, let's get on with the tour. Stop #1: NIF. As we arrived at the NIF, we saw a big sign saying "Bringing star power to earth." Let me explain what that means. The NIF is a nuclear fusion research facility that houses 192 lasers, including four of the most powerful lasers in the world. Using the Inertial Confinement Fusion technology developed at LLNL, these lasers are used to ignite a fusion reaction (at a temperature of 100,000,000 degrees) that creates a miniature sun (a medium sized star) inside a 10 meter spherical target chamber (see photo from NIF). The fusion chain reaction in this miniature sun will then generate clean energy much like the sun has power life on earth for millions of years. This animation by NIF explains how these lasers work.

Current nuclear power plants use nuclear fission technology, which produces a radioactive byproduct that remains hazardous for millennia and can be used in nuclear weapons. However, the byproduct of fusion is just Helium-4, a perfectly clean, safe, non-reactive noble gas. Moreover, fusion technology could eliminate our dependence on fossil fuels, because fusion power is much more efficient. The fusion energy released from 1 gram of hydrogen fuel is equivalent to roughly 2 tons of coal!

The NIF is a colossal project! Commissioned in 1993, the construction of NIF began in 1997, took 12 years and $3.5 billion. The last of the 192 laser beams were completed on March 31, 2009. I feel so privileged to be able to see the NIF in its entirety so soon after its completion. Currently, hundreds of engineers and scientists at NIF are using simulation shots to test all the lasers, the diagnostics, and the control system. There is absolutely zero tolerance for errors. Future experiments are aimed at replicating the nuclear ignition process reliably and rapidly enough so that it can be a viable source of commercial energy.

Are you excited? As a scientist, I'm totally thrilled! You may admire the futuristic look of this magnificent structure, but what I see is the culmination of a Herculean scientific endeavor. To me, the NIF is a product of thousands of passionate scientists and engineers from all sorts of backgrounds who have dedicated their lives to working together for the well being of humanity. I just couldn't help but be in awe when I think about the difficulties they must have overcome to reach this stage.

Let's save some of my enthusiasm for later. This is the first time I have posted something unrelated to social analytics, but I thought it might be interesting for you to get a peek at the life of a scientist. Let me know how you like my little adventure. Stay tuned at mich8elwu.

Hello, TGIF. I think this is the first time I've try posting a blog on a Friday. I had a hectic week!

So, this is the fifth and the last article in the miniseries Ranks Designed to "Flow". Previous blog articles from this miniseries can be access through these links:

1. Spacing the rungs of your ranking ladder

2. Know your superusers!

3. Flow with your most prolific superusers

4. Give your superusers a little surprise

We've covered a lot of ground, and I've introduced many design principles for building an optimal ranking ladder for engaging your superusers. I must emphasize that it is very important to implement these rank design principle in the order that they are presented. It is meaningless to flow with your superusers, if you don't know your superusers' capability. And it is useless trying to surprise your superusers with special privileges if the gaps between your ranks are so large that it takes them years to get a promotion. They will never get there and never be surprised! However, the first two steps do involve some analytical work, and they are the most difficult and most important step (especially step 2). Once you know your superusers, everything that follows is easy.

Building a complete ranking structure

Until now, I have been talking about the principles for designing a single ranking ladder that rewards the posting behavior of community members. Although message posting is a common participation within online communities, modern community platforms now furnish their members with a host of activities. Consequently, superusers may come in many flavors depending on the kinds of activity they participate in. The superusers that we've considered so far are content creators who excel in posting messages. But superusers may be critics who rate contents by giving kudos and report inappropriate contents, and others may be organizers who label and tag contents. Therefore, an ideal ranking structure should have multiple branches for rewarding different kinds of participation.

To create a multi-branch ranking structure, you simply juxtapose everal ranking ladders together. Each ladder has a set of ranking criteria that is based on different participation metrics. For example, rather than post requirement, some ladders may use kudos requirements, and others may have a tag count requirement. Based on the superusers' participation, they will climb different ladders. Some well-rounded superusers may even excel on several ladders. Moreover, ranking ladders can be merged by creating participation criteria using the logical AND.

So how many branches should you have? According to Forrester Research, online participation pattern can be segmented into 6 categories via the social technographic profile: inactives, spectators, joiners, collectors, critics, and creators. Since inactives do not participate, and spectators only consume contents passively, there are at least 4 categories of active participation that you can reward. But in theory, there is no limit to how many branches you could have in your ranking structure. The more ladders you have, the more unique your superusers will feel about their contribution (and reward). But more ladders required more management. A multi-branch ranking structure should be the last step in the design of your ranking structure. Having many poorly designed ranking ladders is much worse than having one that is well designed. My advice is to start with one ladder for your creators. When you are able to manage steps 1 to 4 with all the yearly adjustment, add one for your critics, then collectors, and finally joiners.

Congratulation! This concludes my miniseries on the optimal design of your ranking structures. Next time we'll explore something different. Have a great weekend.

Photo by Jon Wiley