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Inferring Tie Strength from Online Directed Behavior

Overview of attention for article published in PLOS ONE, January 2013
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6 news outlets
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61 X users
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203 Mendeley
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Title
Inferring Tie Strength from Online Directed Behavior
Published in
PLOS ONE, January 2013
DOI 10.1371/journal.pone.0052168
Pubmed ID
Authors

Jason J. Jones, Jaime E. Settle, Robert M. Bond, Christopher J. Fariss, Cameron Marlow, James H. Fowler

Abstract

Some social connections are stronger than others. People have not only friends, but also best friends. Social scientists have long recognized this characteristic of social connections and researchers frequently use the term tie strength to refer to this concept. We used online interaction data (specifically, Facebook interactions) to successfully identify real-world strong ties. Ground truth was established by asking users themselves to name their closest friends in real life. We found the frequency of online interaction was diagnostic of strong ties, and interaction frequency was much more useful diagnostically than were attributes of the user or the user's friends. More private communications (messages) were not necessarily more informative than public communications (comments, wall posts, and other interactions).

X Demographics

X Demographics

The data shown below were collected from the profiles of 61 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 203 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 4 2%
France 3 1%
United States 3 1%
United Kingdom 3 1%
Portugal 1 <1%
Australia 1 <1%
Italy 1 <1%
Ireland 1 <1%
Russia 1 <1%
Other 3 1%
Unknown 182 90%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 61 30%
Student > Master 34 17%
Researcher 28 14%
Professor 14 7%
Student > Bachelor 11 5%
Other 34 17%
Unknown 21 10%
Readers by discipline Count As %
Computer Science 55 27%
Social Sciences 52 26%
Business, Management and Accounting 12 6%
Psychology 12 6%
Medicine and Dentistry 7 3%
Other 38 19%
Unknown 27 13%