(Not) Too much information: IBM’s Social Lens prototype lets users create filters to find just enough relevant information about topics that interest them.
The research is still a long way from becoming a product, but tests so far have been encouraging. Michael Muller, an expert in collaborative software who is involved with the project, says that a small pilot user study with IBM employees found that people scored posts from Social Lens as most interesting, compared with posts retrieved from within a user’s social network and with a simple feed of recent posts. What’s more, Muller notes, 47 percent of the content the tool found came from outside a user’s social network, which suggests it was finding information the user might not have come across otherwise. Of that new information, users regarded 62 percent of it as very relevant.
Social Lens is far from the only such project, even within IBM. At the same event, the company demonstrated Audrey, a system that tries to solve the same problem by focusing on personalization. The Palo Alto Research Center’s Augmented Social Cognition team is also developing tools that can help business users navigate the landscape of social media efficiently, and Microsoft’s Fuse Labs is conducting similar experiments.
Technologies to refine the flow of updates are definitely needed to help people work efficiently, says Joanne Cantor, outreach director of the Center for Communication Research at the University of Wisconsin-Madison, and author of the book Conquer Cyberoverload. “Our brains are designed to want to get all of these and not be able to ignore them when they come in,” she says. “But all of these interruptions tend to interfere with our ability to get work done and be creative.”
Cantor cautions that many tools for cutting back information aren’t very user-friendly and don’t get adopted. People are also reluctant to trust that the tool will work properly without losing important updates as it filters them.
Daniel Tunkelang, an engineer at Google who is an expert on information retrieval, says he likes the ideas behind Social Lens–filtering by topic makes particular sense in a business environment because of the way employees shift from project to project, and lenses could be great to share with new hires or new team members. However, he says, he worries about the effort required for users to set up a lens. To get more work out of users, he says, a system would have to really reward people for their extra effort. On the other hand, says Tunkelang, Social Lens might be successful if a few committed people created lenses that they then shared throughout their organizations.
Muller acknowledges that Social Lens needs to become easier to use, and that it needs more testing, particularly to determine whether it works better than other approaches to filtering.