Truth decayFake social media followers may derail the booming influencer marketing business

Celebrities, social media stars, and other online personalities have taken a hit to their credibility in recent months, as millions of their followers have been exposed as fake or bought. This has created a bigger problem for advertisers and consumers, who no longer can trust in high follower numbers as a measure of influence and credibility.

Northeastern News reports that now, a machine-learning algorithm developed by Northeastern graduates is giving marketers a way to keep their advertising real—and rebuild consumer trust.  Brands have always sought celebrity endorsements, but the mass adoption of social media has given rise to a new kind of endorser: the influencer, an online personality with a large number of followers.

“Influencers are a whole new kind of currency,” said Lyle Stevens, chief executive officer for Mavrck, an influencer marketing firm in Boston that he founded with two other Northeastern graduates, Sean Naegeli and Chris Wolfel.

The company’s influencer marketing platform is used today by P&G, PepsiCo, and other major consumer brands. Big brands such as these use influencers to push their products. Mavrck built a platform that allows brands to tap into their social networks and identify influencers who can then be inspired to share relevant content in their newsfeeds. Identifying whether influencers have an inflated number of followers is one of the many tools the platform provides.

When social media personalities who promote products bolster their numbers with fake followers, they create anillusion of popularity, and with it an illusion of their ability to influence consumers’ buying choices, putting the entire field of influencer marketing into question. At a time when consumers trust each other’s opinions more than they trust brands, Stevens sees this issue of credibility as “a lynchpin to unlocking the full potential of influencer marketing.”

Mavrck’s new fraud detection algorithm works by analyzing a statistically significant sample of an influencer’s followers, as well as accounts that ‘like’ or comment on that influencer’s posts. The algorithm gauges whether an account is being run by a real person or operated by a bot to create artificial followers, likes, and comments. This kind of analysis helps marketers automate the process of weeding out those accounts that are likely to have purchased followers or engagements.