A recent study by Stanford Business School researchers found that reaching people through social media influencers can be the same as reaching people through random social media users.
“Find people who hold the most influence, typically those who sit at the centre of a social network — the hub in a wheel — and “seed” them with the new information. From there, the idea will efficiently reach new ears through word-of-mouth,” the note about the study described the process of using social media influencers.
Unfortunately, finding these hubs can be a lengthy and expensive process. Picking the five best seeds in a 200-person network requires checking 2.5 billion variations. The note said, consider a network of 1,000 people or 1 million people, and this means that there is a more straightforward approach.
A team of Stanford researchers found a remarkable result: Simply seeding a few more people at random avoids the challenge of mapping a network’s contours and can spread information in a way that is essentially indistinguishable from cases involving careful analysis; seeding seven people randomly may result in roughly the same reach as seeding five people optimally. The results available in their online working paper, “Just a Few Seeds More: Value of Network Information for Diffusion,” are surprising.
Deliberate seeding efforts rely on the degree to which somebody sits at the centre of a network. They look for the people who are most highly connected and thus best positioned to spread information. But this approach can create redundancy that leads to rapidly diminishing returns,”Stanford Business report
This means that social media advertising could work just as well if random people were selected to advertise the product rather than the company’s careful selection process.
Some people could argue that companies select social media users who significantly influence other users and have a huge number of followers. According to the study, this method can create diminishing returns.
Network information can be super expensive to collect, and finding precisely the right people to help something go viral is unpredictable. You might be better just ignoring the network altogether and seeding a few more people, Mohammad Akbarpour an assistant professor of economics at Stanford Graduate School of Business and one of the authors of this study said.
“The interesting thing is that if you use an algorithm that targets people with the most friends, you will pick people who are likely connected to the core of the network,” Saberi says. “And once you’ve talked to a few of these people, the next one will not be as valuable, since you’ve already saturated the core.”
In the meantime, you’ve ignored a bunch of so-called “small communities” — satellite networks on the periphery that are loosely connected to the main network. “This is not to recommend random seeding as a universal policy,” Saberi says, “but to show that central individuals do not always maximize diffusion.”
Could this have happened during the social media promotions of ‘Audible’? Every influencer on the network was promoting Amazon’s Audible and soon the comments sections were filled with people hating the product since all they were watching were people promoting audible. Rather than become a positive story, it became a case of negative publicity.