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:
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