Information Stream Overload

Information overload is a growing threat to the productivity of today’s knowledge workers, who need to keep track of multiple streams of information from various sources. RSS feed readers are a popular choice for syndicating information streams, but current tools tend to contribute to the overload problem instead of solving it.  Ironic, isn't it?

A significant portion of the ASC team is here in Atlanta to present work related to this information overload problem, and I will blog about it in the next week or so.

Tomorrow, we will be presenting a paper on FeedWinnower, an enhanced feed aggregator that helps readers to filter feed items by four facets (topic, people, source, and time), thus facilitating feed triage. The four facets corresponds to the What, When, Who, and When questions that govern much information architecture design.  The combination of the four facets provides a powerful way for users to slice and dice their personal feeds.

First, a topic panel allows users to drill down into the specific topics that she might be interested in:

Second, a people panel allows filtering on the source of the person who created the information item in the stream:

Third, a source panel allows filtering of the type of information stream the item came from:

And finally, a time panel allows filtering for a particular time period that you might be interested in out of the information stream:

Usage Scenarios
By combining the four facets, users can examine and navigate their feeds, deciding what items to skip and what to read. Here we give two illustrative real-world scenarios.

Scenario 1: At the end of a workday, Mary opens FeedWinnower to get a sense of what has been happening around her. Using the time facet, she finds out that 507 items came into her account earlier in the day. Glancing at the topic facet, she sees “iphone” and a few other topics being talked about. As she clicks on “iphone”, the right screen shows only 7 items after filtering out other items. In the people facet, she identifies that these 7 items came from 4 of her friends and decides to read those items in detail.

Scenario 2: John wants to find out what his friends have been chatting about on Twitter lately. He selects “Twitter” in the source facet and chooses “yesterday” in the time facet. This yields 425 items. In the people facet, he then excludes those creators that he wants to ignore, filtering down to 324 items. Looking at the topic facet, he sees “betacup” and wonders what it is about. After clicking on “betacup” and reading the remaining 7 items, he now has a fair understanding about the term “betacup”.
In these two scenarios, we see how the four facets enable users to construct simple queries to accomplish their needs. We also see how the topic facet is essential in obtaining an overview of the topical trends in the feeds and helping users to decide what is worth reading in depth.

The paper reference is:
Hong, L., Convertino, G., Suh, B., Chi, E. H., and Kairam, S. 2010. FeedWinnower: layering structures over collections of information streams. In Proceedings of the 28th international Conference on Human Factors in Computing Systems(Atlanta, Georgia, USA, April 10 - 15, 2010). CHI '10. ACM, New York, NY, 947-950. DOI= http://doi.acm.org/10.1145/1753326.1753466

What are big research problems in Social Web technologies?

Just finished reading Dion Hichcliffe's piece over at ZDNet on emerging technologies for Social Web in 2010. I have been reading all these different predictions to see how it relates to our research agenda. Dion's piece is long, but several points resonated with what we have been doing:

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 [1]. 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 [2].

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 [3].

I will blog about these research efforts soon.

----
[1] 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.

[2] 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.

[3] 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.