Skip to Content

Fake Followers for Hire, and How to Spot Them

It’s possible to buy a good reputation on the Internet for a modest price, but some are trying to put an end to that.

Given that Twitter followers and Facebook likes are one measure of popularity, it can be tempting to fudge the numbers. And that is cheap and easy to do, thanks to a willing cyber workforce dedicated to building fake reputations.

New research provides a fresh measure of the black market for creating false online reputations, but it also highlights a way to curb it.

“Crowdturfing,” as the phenomenon is known, is a combination of “crowdsourcing,” meaning recruiting large numbers of people to contribute a small effort each toward a big task (like labelling photos), and “astroturfing,” meaning false grassroots support (in the form of bogus reviews or comments, for example).

A team of researchers at UC Santa Barbara, led by Ben Zhao, coined the term three years ago, when they also showed that this constituted more than 80 percent of the activity, worth several million dollars, on two prominent crowdsourcing websites in China. Zhao’s group also found crowdturfing activity on several U.S. websites, including ShortTask, MinuteWorkers, MyEasyTask, and Microworkers. Several of the jobs posted this month on ShortTask, for example, ask workers to follow someone specific on Twitter, while others ask them to like and comment on a particular video on YouTube.

Now, in a new paper, Zhao and his team show a way to identify crowdturfing using machine learning software. The software learned to use 35 account characteristics, such as age and location, to recognize crowdturfers on China’s version of Twitter with 95 to 99 percent accuracy.

Zhao isn’t the only researcher studying crowdturfing. Earlier this month, a group led by Kyumin Lee at Utah State University published an analysis of Fiverr, a U.S. website where people post “gigs” they are willing to do for five dollars: paint a portrait, make personalized lip balm, write a press release, etc.

Lee and his collaborators found that nine out of the top 10 sellers on Fiverr were crowdturfing—selling Twitter followers, website traffic, or likes on Facebook. The top seller, who goes by the username Crorkservice, had performed more than 600,000 gigs and made at least $3 million in just two years, the researchers say. Lee’s group also developed software capable of detecting crowdturfing by analyzing key features of a gig; its accuracy rate was 97 percent. Fiverr did not return requests for comment.

Keep Reading

Most Popular

AV2.0 autonomous vehicles adapt to unknown road conditions concept
AV2.0 autonomous vehicles adapt to unknown road conditions concept

The big new idea for making self-driving cars that can go anywhere

The mainstream approach to driverless cars is slow and difficult. These startups think going all-in on AI will get there faster.

biomass with Charm mobile unit in background
biomass with Charm mobile unit in background

Inside Charm Industrial’s big bet on corn stalks for carbon removal

The startup used plant matter and bio-oil to sequester thousands of tons of carbon. The question now is how reliable, scalable, and economical this approach will prove.

images created by Google Imagen
images created by Google Imagen

The dark secret behind those cute AI-generated animal images

Google Brain has revealed its own image-making AI, called Imagen. But don't expect to see anything that isn't wholesome.

AGI is just chatter for now concept
AGI is just chatter for now concept

The hype around DeepMind’s new AI model misses what’s actually cool about it

Some worry that the chatter about these tools is doing the whole field a disservice.

Stay connected

Illustration by Rose WongIllustration by Rose Wong

Get the latest updates from
MIT Technology Review

Discover special offers, top stories, upcoming events, and more.

Thank you for submitting your email!

Explore more newsletters

It looks like something went wrong.

We’re having trouble saving your preferences. Try refreshing this page and updating them one more time. If you continue to get this message, reach out to us at customer-service@technologyreview.com with a list of newsletters you’d like to receive.