Major LinkedIn study shows the value of weak ties when searching for a job

Say you are looking for a new job. You head to LinkedIn to spruce up your profile and look around your social network.

But who should you reach out to for an introduction to a potential new employer? A new study of more than 20 million people, published in Science, shows that your close friends (on LinkedIn) are not your best bet: instead you should look to acquaintances you don’t know well enough to share a personal connection with.

The strength of weak ties

In 1973, the American sociologist Mark Granovetter coined the phrase “the strength of weak ties” in the context of social networks. He argued that the stronger the ties between two individuals, the more their friendship networks will overlap.

Simply put, you are most likely to know all the friends of a close friend, but few of the friends of an acquaintance.

So if you are searching for a job, you probably already know everything your immediate neighbourhood has to offer. Intuitively, it is the weak ties — your acquaintances — that offer the most opportunities for new discoveries.

Weak ties and jobs

Granovetter’s theory feels right, but is it? A team of researchers from LinkedIn, Harvard Business School, Stanford and MIT set out to gather some empirical evidence on how weak ties affect job mobility.

Their research piggy-backed on the efforts of engineers at LinkedIn to test and improve the platform’s “People You May Know” recommendation algorithm. LinkedIn regularly updates this algorithm, which recommends new people to add to your network.

One of these updates tested the effects of encouraging the formation of strong ties (recommending adding your close friends) versus weak ties (recommending acquaintances and friends of friends).

The researchers then followed the users that participated in this “A/B testing” to see if the difference impacted their employment outcomes.

More than 20 million LinkedIn users worldwide were randomly assigned to well-defined treatment groups.

Users in each group were shown slightly different new contact recommendations, which led users in some groups to form more strong ties and users in other groups to form more weak ties.

Next, the team measured how many jobs users in each group applied for, and how many “job transmissions” occurred. Job transmissions are of particular interest, as they are defined as getting a job in the same company as the new contact. A job transmission suggests the new contact helped land the job.

Moderately weak ties are best

The study uses causal analysis to go beyond simple correlations and connect link formation with employment. There are three important findings.

First, the recommender engine significantly shapes link formation. Users who were recommended more weak links formed significantly more weak links, and users who were recommended more strong links formed more strong links.

Second, the experiment provides causal evidence that moderately weak ties are more than twice as effective as strong ties in helping a job-seeker join a new employer. What’s a “moderately” weak tie? The study found job transmission is most likely from acquaintances with whom you share about 10 mutual friends and rarely interact.

Third, the strength of weak ties varied by industry. Whereas weak ties increased job mobility in more digital industries, strong ties increased job mobility in less digital industries.

Better recommendations

This LinkedIn study is first to causally prove Granovetter’s theory in the employment market. The causal analysis is key here, as large-scale studies of correlations between strength of ties and job transmission have shown strong ties are more beneficial, in what was considered until now a paradox.

This study resolves the paradox and again proves the limitations of correlation studies, which do a poor job at disentangling confounding factors and sometimes lead to the wrong conclusions.

From a practical point of view, the study outlines the best parameters for suggesting new links. It revealed that the connections most helpful in landing a job are your acquaintances, people you meet in professional settings, or friends of friends, rather than your closest friends — people with whom you share about 10 mutual contacts and with whom one is less likely to interact regularly.

These can be translated into algorithmic recommendations, which can make the recommendation engines of professional networks such as LinkedIn even more proficient at helping job-seekers land jobs.

The power of black boxes

The public is often wary when large social media companies perform experiments on their users (see Facebook’s infamous emotion experiment of 2014).

So, could LinkedIn’s experiment have harmed its users? In theory, the users in the “strong link” treatment group might have missed the weak links that could have brought their next job.

However, all groups had some degree of job mobility — some just a bit more than others. Moreover, since the researchers were observing an engineering experiment, the study itself seems to raise few ethical concerns.

Nonetheless, it is a reminder to ask how much our most intimate professional decisions — such as selecting a new career or workplace — are determined by black-box artificial intelligence algorithms whose workings we cannot see.

Marian-Andrei Rizoiu, is a senior lecturer in behavioral data science at the University of Technology Sydney.

This article is republished from The Conversation under a Creative Commons license. Read the original article.