Featured Research

from universities, journals, and other organizations

What online social networks may know about non-members

Date:
April 30, 2012
Source:
Heidelberg, Universität
Summary:
What can social networks on the internet know about persons who are friends of members, but have no user profile of their own? Researchers have just studied this question. Their work shows that through network analytical and machine learning tools the relationships between members and the connection patterns to non-members can be evaluated with regards to non-member relationships. Using simple contact data, it is possible, under certain conditions, to correctly predict that two non-members know each other with approx. 40 percent probability.

Any social network platform divides society into members and non-members. Relationships between non-members whose e-mail contact has been revealed by a member (red lines) can be accurately inferred based on relationships between members (black lines) and their connection patterns to non-members (green lines).
Credit: Picture by Ágnes Horvát

What can social networks on the internet know about persons who are friends of members, but have no user profile of their own? Researchers from the Interdisciplinary Center for Scientific Computing of Heidelberg Univer­sity studied this question. Their work shows that through network analytical and machine learning tools the relationships between members and the connection patterns to non-members can be evaluated with regards to non-member relationships. Using simple contact data, it is possible, under certain conditions, to correctly predict that two non-members know each other with approx. 40 percent probability.

For several years scientists have been investigating what conclusions can be drawn from a computational analysis of input data by applying adequate learning and prediction algorithms. In a social network, information not disclosed by a member, such as sexual orientation or political preferences, can be "calculated" with a very high degree of accuracy if enough of his or her friends did provide such information about themselves. "Once confirmed friendships are known, predicting certain unknown properties is no longer that much of a challenge for machine learning," says Prof. Dr. Fred Hamprecht, co-founder of the Heidelberg Collaboratory for Image Pro­cessing (HCI).

Until now, studies of this type were restricted to users of social networks, i.e. persons with a posted user profile who agreed to the given privacy terms. "Non-members, however, have no such agreement. We therefore studied their vulnerability to the automatic generation of so-called shadow profiles," explains Prof. Dr. Katharina Zweig, who until recently worked at the Interdisciplinary Center for Scientific Computing (IWR) of Heidelberg University.

In an online social network, it is possible to infer information about non-members, for instance by using so-called friend-finder applications. When new Facebook members register, they are asked to make available their full list of e-mail contacts, even of those people who are not Facebook members. "This very basic knowledge of who is acquainted with whom in the social network can be tied to information about who users know outside the network. In turn, this association can be used to deduce a substantial portion of relationships between non-members," explains Ágnes Horvát, who conducts research at the IWR.

To make their calculations, the Heidelberg researchers used a standard procedure of machine learning based on network analytical structural properties. As the data needed for the study was not freely obtainable, the researchers worked with anonymised real-world Facebook friendship networks as a test set of basic data. The partitioning between members and non-members was simulated using a broad possible range of models. These partitions were used to validate the study results. Using standard computers the researchers were able to calculate in just a few days which non-members were most likely friends of each other.

The Heidelberg scientists were astonished that all the simulation methods produced the same qualitative result. "Based on realistic assumptions about the percentage of a population that are members of a social network and the probability with which they will upload their e-mail address books, the calculations enabled us to accurately predict 40 percent of the relationships between non-members." According to Dr. Michael Hanselmann of the HCI, this represents a 20-fold improvement compared to simple guessing.

"Our investigation made clear the potential social networks have for inferring information about non-members. The results are also astonishing because they are based on mere contact data," emphasises Prof. Hamprecht. Many social network platforms, however, have far more data about users, such as age, income, education, or where they live. Using this data, a corresponding technical infrastructure and other structural properties of network analysis, the researchers believe that the prediction accuracy could be significantly improved. "Overall our project illustrates that we as a society have to come to an understanding about the extent to which relational data about persons who did not provide their consent may be used," says Prof. Zweig.


Story Source:

The above story is based on materials provided by Heidelberg, Universität. Note: Materials may be edited for content and length.


Journal Reference:

  1. Emöke-Ágnes Horvát, Michael Hanselmann, Fred A. Hamprecht, Katharina A. Zweig. One Plus One Makes Three (for Social Networks). PLoS ONE, 2012; 7 (4): e34740 DOI: 10.1371/journal.pone.0034740

Cite This Page:

Heidelberg, Universität. "What online social networks may know about non-members." ScienceDaily. ScienceDaily, 30 April 2012. <www.sciencedaily.com/releases/2012/04/120430114905.htm>.
Heidelberg, Universität. (2012, April 30). What online social networks may know about non-members. ScienceDaily. Retrieved April 23, 2014 from www.sciencedaily.com/releases/2012/04/120430114905.htm
Heidelberg, Universität. "What online social networks may know about non-members." ScienceDaily. www.sciencedaily.com/releases/2012/04/120430114905.htm (accessed April 23, 2014).

Share This



More Computers & Math News

Wednesday, April 23, 2014

Featured Research

from universities, journals, and other organizations


Featured Videos

from AP, Reuters, AFP, and other news services

Monkeys Are Better At Math Than We Thought, Study Shows

Monkeys Are Better At Math Than We Thought, Study Shows

Newsy (Apr. 23, 2014) — A Harvard University study suggests monkeys can use symbols to perform basic math calculations. Video provided by Newsy
Powered by NewsLook.com
High Court to Hear Dispute of TV Over Internet

High Court to Hear Dispute of TV Over Internet

AP (Apr. 22, 2014) — The future of Aereo, an online service that provides over-the-air TV channels, hinges on a battle with broadcasters that goes before the U.S. Supreme Court on Tuesday. (April 22) Video provided by AP
Powered by NewsLook.com
Aereo Takes on Broadcast TV Titans in Supreme Court Today

Aereo Takes on Broadcast TV Titans in Supreme Court Today

TheStreet (Apr. 22, 2014) — Aereo heads to the Supreme Court today to fight for its right to stream broadcast TV over the Internet -- against broadcasters who say the start-up infringes upon copyright law. TheStreet Deputy Managing Editor Leon Lazaroff explains the importance of the case in the TV industry and details what the outcome of it could mean for broadcasters and for cloud storage services -- as Aereo allows its subscribers to not just watch live TV shows but also store content to a DVR in the cloud. Video provided by TheStreet
Powered by NewsLook.com
Lytro Introduces 'Illum,' A Professional Light-Field Camera

Lytro Introduces 'Illum,' A Professional Light-Field Camera

Newsy (Apr. 22, 2014) — The light-field photography engineers at Lytro unveiled their next innovation: a professional DSLR-like camera called "Illum." Video provided by Newsy
Powered by NewsLook.com

Search ScienceDaily

Number of stories in archives: 140,361

Find with keyword(s):
 
Enter a keyword or phrase to search ScienceDaily for related topics and research stories.

Save/Print:
Share:  

Breaking News:
from the past week

In Other News

... from NewsDaily.com

Science News

Health News

Environment News

Technology News



Save/Print:
Share:  

Free Subscriptions


Get the latest science news with ScienceDaily's free email newsletters, updated daily and weekly. Or view hourly updated newsfeeds in your RSS reader:

Get Social & Mobile


Keep up to date with the latest news from ScienceDaily via social networks and mobile apps:

Have Feedback?


Tell us what you think of ScienceDaily -- we welcome both positive and negative comments. Have any problems using the site? Questions?
Mobile iPhone Android Web
Follow Facebook Twitter Google+
Subscribe RSS Feeds Email Newsletters
Latest Headlines Health & Medicine Mind & Brain Space & Time Matter & Energy Computers & Math Plants & Animals Earth & Climate Fossils & Ruins