LinkedIn ran experiments for five years with more than 20,000,000 users . Between 2015 and 2019, the platform conducted random surveys based on the “People You Might Know” algorithm, through which friend recommendations are made to platform members.
The results of the research, which was carried out in conjunction with the Massachusetts Institute of Technology (MIT), Stanford University and the Harvard Business School, were published in the journal .
The work analyzes “the influence of weak associations”, a social scientific theory that highlights the importance of weak associations -such as acquaintances versus close friendships- in influencing the transmission of information through social networks.
Experiments showed that weak ties increase work streams, but only up to a certain point, after which there are diminishing marginal returns to weak ties.
The authors show that the weakest ties, that is, known ties, had the greatest impact on labor mobility, while the strongest ties had the least.
In summary, the research suggests that there are more possibilities of obtaining better job opportunities through an acquaintance (weak bond), than through those closest to us .
question data usage
Although LinkedIn maintains that the research improves labor mobility on the platform, it has also been questioned for not informing its users about the experiment, since in addition to the use of data, it has been questioned whether some users lost opportunities over others who benefited more due to thereto.
“The findings suggest that some users had better access to job opportunities or a significant difference in access to job opportunities,” said Michael Zimmer, associate professor of computer science and director of the Center for Data, Ethics and Society at the University of Marquette.
Sinan Aral, lead author of the study and a professor at MIT, defended the tests, arguing that LinkedIn is simply trying to find a more useful algorithm for its users.
What does Linkedin say?