A Study on Aggregate Data and Social Network Design

Duncan Watts, a sociology professor at Columbia University, conducted a fascinating study on how aggregate data influences the way people interact and make decisions on social network sites.

Aggregate data—also known as a plug-in or widget—is a common element of social network design. Examples of this data include items like top 10 lists, most-emailed articles, most read articles, etc…. The study was attempting to determine how much this data influences people and the decisions they make because of the data.

For the sociology experiment, the researchers built two websites. On one site, users were shown a list of songs. They were given the opportunity to rate songs and download any songs—whether they did any rating or not. On the second website, users were also shown a song list. However, on the second site, the number of times each song was downloaded was displayed to the users—as aggregate data. The first group was known as the independent group and the second was called the social influence group. The social influence group was further divided into 8 different sub-groups to see if there were differences over time.

If the aggregate data—the number of times each song was downloaded in this case—had no influence, several results would have shown up. First, the songs downloaded would have been approximately the same for both groups. Second, the songs downloaded in the 8 subgroups would have followed the same patterns as shown in the larger group. The results proved otherwise.

The most downloaded songs in the social influence group were more popular—as shown by more downloads—than in the independent group. The assumption is that users downloaded songs which had the higher download numbers. In essence, each download proved to be a vote for a song, whether the song was good or not. What was more interesting is that each of the most downloaded songs in the 8 sub-groups was different. Songs downloaded first remained the leaders throughout the experiment.

The results show that the social influence had a far greater effect on the number of downloads compared to the independent decisions by the individuals. Whether users downloaded the songs because they actually liked the song or because they assumed everyone else was right is ripe for another study.

However, there are far-reaching repercussions for social network design as a result of this study. First, we know that dynamic data influences users. Second, we know that aggregate data can have a much larger impact than we expect. These factors may not be able to predict how consumers behave in a social network environment, but we know they have deeper implications than first thought.

 

Written by: David C Skul - CEO

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