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Facebook App Shoppers Do What Their Friends Do

People on Facebook are more influenced by their immediate community than by popular opinion.
(Photo: Lee O'Dell/Shutterstock)

(Photo: Lee O'Dell/Shutterstock)

Say you’re deciding between downloading FarmVille 2 and Candy Crush Saga. Are you more likely to buy the game you’ve researched and read good reviews about, or are you prone to picking the one you know your friends have already purchased? Even if you think you’d stay objective and insusceptible to peer influence, you’d probably go with what your friends already have.

That’s the conclusion of a new study by researchers at Oxford, Harvard, and the University of Limerick. Published last month in Proceedings of the National Academy of Sciences, the paper showed that people who shop for Facebook apps are more influenced by their friends’ choices than by an app’s overall popularity.

Decisions about what to buy were more heavily influenced by what friends had recently bought than by any bestseller list.

Using Facebook data from 2007, the researchers looked at the number of times people downloaded applications on that social network and, based on a mathematical model they created, tracked the apps’ popularity, hour by hour, over the course of a few months. At the beginning of data period, 980 Facebook apps were available for download. By the end, there were 2,705, and the researchers paid particular attention to those that launched during the later portion of the data set.

That whole time, Facebook users had access to two kinds of information about apps they were shopping for: a “cumulative information” stream, which told them which were bestsellers, and a “recent activity information” stream, which told them which their friends had recently installed. Shoppers could also go to their friends’ profiles to see what their pals had bought.

Experts have pointed to two streams of thought when it comes to modeling popularity. In the first, “cumulative advantage," popular opinion reigns supreme, leading to a rich-get-richer type of virality—as in, a tweet retweeted a million times is more likely to get retweeted a million more times. In the second model, people who are members of a community, as the paper puts it, “randomly copy the choices made by other members in the recent past,” creating a situation in which “products whose popularity levels have recently grown the fastest are the most likely to be selected, whether or not they are the most popular overall.”

The researchers found that the second model held truer when it came to downloading Facebook apps: Decisions about what to buy were more heavily influenced by what friends had recently bought than by any bestseller list. (Presumably, though, some of these apps are inherently tied to an existing social network, since people often buy games specifically because they want to play them with their Facebook friends.)

Incidentally, this paper is part of an burgeoning field called “computational social science,” which uses data to determine how people behave online. Often, scholars will set up their own miniature social networks to test how users will act. “It is challenging to run experiments in online environments that people actually use,” the researchers note, “as opposed to creating new online environments with potentially distinct behaviors.”

Rosie Spinks contributed reporting.