University of Pittsburgh

Twitter is the new TV

New research from scholars at Columbia Business School and the University of Pittsburgh questions the sustainability of Twitter, the social network that has more than 500 million registered users. The research was recently published in the journal Marketing Science.

Columbia Business School Professor Olivier Toubia has a thought-provoking, 140-character-limit comment about the research he co-authored with University of Pittsburgh’s Assistant Professor Andrew T. Stephen.

“Get ready for a TV-like Twitter,” said Toubia.

The research examined the motivations behind why everyday people, with no financial incentive, contribute to Twitter.

The study examined roughly 2500 non-commercial Twitter users. In a field experiment, Toubia and Stephens randomly selected some of those users and, through the use of other synthetic accounts, increased the selected group’s followers. At first, Toubia and Stephen noticed that as the selected group’s followers increased, so did the posting rate. However, when that group reached a level of stature — a moderately large amount of followers — the posting rate declined significantly.

Based on the analyses, Toubia and Stephen predict Twitter posts by everyday people will slow down, yet celebrities and commercial users will continue to post for financial gain.

“Twitter will become less of a communications vehicle and more of a content-delivery vehicle, much like TV. Peer-to-peer contact is likely to evolve to the next great thing, but with 500 million followers, Twitter isn’t just going to disappear. It’s just going to become a new way to follow celebrities, corporations, and the like,” said Toubia.

Friend or Foe

The mutual-friends feature on social networks such as Facebook, which displays users’ shared friendships, might not be so “friendly.”

Often revered for bringing people together, the mutual-friends feature on Facebook actually creates myriad security risks and privacy concerns according to a University of Pittsburgh study. The study demonstrates that even though users can tailor their privacy settings, hackers can still find private information through mutual-friends features.

Using computer simulation programs and an offline Facebook dataset containing 63 731 users, the researchers first demonstrated a “friend exposure” attack, exploring how many private friends an “attacker” could find of a specific target user. The attacks were tested on 10 randomly chosen user groups with sizes ranging between 500 and 5 000 individuals, as well as sample groups that were computer generated based on shared interests across user profiles. The same process was used for the “distant neighbor exposure attack,” through which the attacker’s goal was to identify private distant neighbors from the initial target. These distant neighbors indicate users that are friends of friends of the target user (two degrees of separation) or even friends of friends of friends of the target user (three degrees of separation).

Finally, the team initiated a “hybrid attack,” in which an attacker tried to identify both the target’s private friends and distant neighbors.

They found that an attacker identified more than 60 percent of a target’s private friends in the “mutual-friend based attack.” Likewise, an attacker could find, on average, 67 percent of a target’s private distant neighbors by using 100 compromised user accounts.

The study shows the need for better privacy-protection settings to mitigate the problem — those that can also be easily navigated by users.

The paper, Mutual-friend Based attacks in Social Network Systems, was first published online April 22 in Computers & Security.