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
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 22 | 36% |
Spain | 3 | 5% |
United Kingdom | 3 | 5% |
Italy | 2 | 3% |
Germany | 2 | 3% |
Canada | 2 | 3% |
Sweden | 1 | 2% |
Ireland | 1 | 2% |
Australia | 1 | 2% |
Other | 3 | 5% |
Unknown | 21 | 34% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 39 | 64% |
Scientists | 17 | 28% |
Practitioners (doctors, other healthcare professionals) | 3 | 5% |
Science communicators (journalists, bloggers, editors) | 2 | 3% |
Mendeley readers
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% |