First, he said that one problem we have is
"Poor integration between social media and location services. Again, while there’s already some location awareness in social networking services today, there’s a long way to go before it’s integrated meaningfully into the social experience to provide real utility."I agree wholeheartedly. Not too long ago, I participated in a research project here at PARC called Magitti, which was an activity recommender that modeled your content interests, your schedule, your location, as well as the your personal history on the mobile device . The integration of personalization and social features with location-aware services will be a significant trend in 2010, and there will be a lot of good research and products in this area.
Second, he said that people are having difficulties in
"coherently engaging in social activity across many channels. Tired of the day-long round-robin between your e-mail, SMS, Twitter, Facebook, and any other services you use to keep up with what’s going on? You’re not the only one. While aggregation services such as Friendfeed potentially cut down on the manual effort of using the social Web, it’s still not mainstream despite being a good example of what’s possible. Notably it’s often the big (and closed) social silos that are causing the problem."Our group was an early adopter of FriendFeed, and realized that many of the issues relating to social annotation, commenting, and other interactions were due to the distributed nature of social media. It is hard to keep track of who said what, and the aggregate reactions to content. Our research group has some investments in this research problem, which relates to aggregation and the ability to browse and filter the feeds. We are about to publish a paper in CHI2010 about how to use faceted browsing techniques to partially solve this problem .
Finally, the most important point he made was the our need in
"Coping with and getting value from the expanding information volume of social media. We’re all learning how to deal with the firehose of information that flows out of social media on a minute-by-minute basis. Sometimes it’s hard to remember that this flow of transparent and open information is actually good and often useful and creates important conversations. But the simple fact is that much of it isn’t meant for non-stop, instantaneous consumption [emphasis added]; it simply isn’t practical. Rather, social media leaves behind artifacts and information that we can find and use later when we need them. But at the moment the process of sorting through, aggregating, and filtering the vast volume of information cascading through social media today remains a real and growing challenge. I also began to get the first real reports that this is happening in the enterprise last year as social media begins to grow there as well."
Here ASC group's investment in summarization, recommendation, and personalization, etc, hopefully will pay off. Our investments have been in understanding particularly how to apply these techniques in social media, with the added social contexts and new data mining techniques around social streams. Research-wise, we will be pushing on this last point the most, and I believe it is also the area we most likely can extract user value. We are about to publish a paper at CHI2010 on how to do recommendations on Twitter network .
I will blog about these research efforts soon.
 Victoria Bellotti, James Bo Begole, Ed H. Chi, Nicolas Ducheneaut, Ji Fang, Ellen Isaacs, Tracy King, Mark Newman, Kurt Partridge, Bob Price, Paul Rasmussen, Michael Roberts, Diane J. Schiano, Alan Walendowski. Activity-Based Serendipitous Recommendations with the Magitti Mobile Leisure Guide. In Proceedings of the ACM Conference on Human-factors in Computing Systems (CHI2008), pp. 1157-1166. ACM Press, 2008. Florence, Italy.
 Hong, L.; Convertino, G.; Suh, B.; Chi, E. H.; Kairam, S. FeedWinnower: layering structures over collections of information streams. Submitted and accepted to ACM CHI2010.
 Chen, J., Nairn, R., Nelson, L., Chi, E. H. Short and Tweet: Experiments on Recommending Content from Information Streams. Submitted and Accepted to ACM CHI2010.