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Supplying Less, Revealing More

Will automatic filters help people deal with information overload?
October 15, 2010

A stockbroker today has access to better up-to-the-minute information about businesses than ever before, but there’s far too much of it. Finding a key piece of information at the right moment could be the ticket to huge returns, but it’s hard for any Web user to know what to focus on.

Feed readers were supposed to save people from losing track of all the information published on the Internet and company networks, but they led to people drowning under thousands of unread posts. Twitter was supposed to solve that problem by allowing people to rely on others to tell them which posts to pay attention to, but must-read recommendations from hundreds of people overwhelmed users again.

Computer scientists at IBM Research are attempting to truly solve the information-overload problem with Social Lens, prototype software that plugs into the company’s in-house social software. It filters updates made on the corporate network–whether they’re internal posts or links to external Web content–to reveal which posts are most relevant. The user starts by choosing a topic and suggesting a few relevant links or people to create a “lens,” which is the filter that narrows down the content. Users can have as many lenses as they want. Then the system finds related content and people who tend to post on the topic. It ranks the results by considering how their source and content relate to the initial suggestions, and then it ranks the initial suggestions to determine which were most essential. This determines what’s most important to show the user.

Elizabeth Daly, one of the researchers involved with the project, says that Social Lens does several unusual things to solve common problems with filters. For one thing, it’s purposely geared to sift information by topic rather than to adapt based on what the user reads; Daly worries that the latter method of personalization often results in one interest dominating the others. The prototype also uses social information, but isn’t tied specifically to a user’s social network–Daly believes there’s an important distinction between friends and sources of useful information. On top of that, for business use, employees sometimes shift interests as they move among projects and groups. To go along with this, lenses aren’t tied to individuals and can be shared among coworkers.

Because Social Lens ranks the relevance of the information it surfaces, Daly says, it includes a slider that users can manipulate to adjust the quantity of information they receive. People can set it to give them only the most important information in an area, or, when they have more time, they can expand the view to see a larger number of posts. Daly hopes the slider will help users who are reluctant to tune a filter for fear of messing it up–this way, they can still limit their view without changing the lens fundamentally.

(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.

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