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Circles, Smart Lists, What’s Next?

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In a direct response to Google Plus’ social networking “Circles” feature, Facebook recently revamped its friend categorization process with the introduction of “Smart Lists.” Both features involve organizing friends into groups. Ideally, these groups make it easier to manage the information users share with different types of “friends” (because we all know that being someone’s Facebook friend can mean anything from best friend to co-worker to that guy you met once three years ago and haven’t spoken to since). What’s more, the groups make it easier to sift through the hundreds of updates that flood users’ news feeds by the hour. Now, users can filter which friends they see updates from, and how often.

Currently, Facebook’s “Smart Lists” feature offers an advantage over Google Plus’ “Circles”: its lists are automatically generated based on users’ self-generated information, such as employer, family, and university affiliation. With Google Plus, users still have to manually drag each ‘friend’ into a circle. Facebook does the heavy lifting for users, an automaticity that seems strikingly similar to Facebook’s friend recommendation service. Labeled on the site as “People You May Know,” the friend recommendation service likely compiles users’ self-generated information and clicking behavior to determine their strong ties (as opposed to weak ties), and thus, which friends-of-friends the site should suggest they friend request. This notion fits with the Strong Triadic Closure Property, in that if one User A has strong ties to another User B, and that other User B has strong ties to a different User C, then Users A and C must have at least a weak tie between them. In terms of Facebook, this weak tie could mean that they are aware of each other’s existence, or perhaps that they have even met in person one or two times but haven’t yet made the leap to becoming Facebook friends. In determining friend recommendations, the strength of mutual friendship ties matters, as does other factors such as frequency of interaction; for example, if User A input her school information and interacts very frequently with other users who are affiliated with her school, Facebook can assume that User A’s strong ties attend her school, and thus might be more inclined to recommend friends of her ‘strong tie’ friends who also attend the same school.

Facebook’s utilization of user-generated information and interaction behavior offers a glimpse at where the future of social networking is headed. In the future, the site will quite probably extend beyond simple categorization of strong and weak ties, and beyond basic lists of schools and companies in common. Instead, I would argue that Facebook’s “Smart Lists” feature  – and friend suggestions as well – will push towards using behavioral information that users consider to be private, or otherwise untraceable. If Facebook hasn’t already implemented a use for this type of information, they will likely start to track and organize users’ friend groups based on clicking behavior and length of time spent looking at certain profiles and pieces of information. For instance, if a user has a pattern of clicking to certain users’ profiles, or frequently scans new photo albums of certain users, perhaps Facebook will create a Smart List for “My Routine” or “Favorite Photos.” While these are just examples, they highlight the potential for smart lists in the future.

But one should also consider the pace at which new features evolve and compete against each other. The advent of “Circles” and now “Smart Lists” raises a larger question than how these lists will function. Perhaps the real question should be: what feature will be next?

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