  Influencer Marketing generated $16 billion in 2022. Usually, the more popular
influencers are paid more for their collaborations. Thus, many services were
created to boost profiles' popularity metrics through bots or fake accounts.
However, real people recently started participating in such boosting activities
using their real accounts for monetary rewards, generating ungenuine content
that is extremely difficult to detect. To date, no works have attempted to
detect this new phenomenon, known as crowdturfing (CT), on Instagram.
  In this work, we propose the first Instagram CT engagement detector. Our
algorithm leverages profiles' characteristics through semi-supervised learning
to spot accounts involved in CT activities. Compared to the supervised
approaches used so far to identify fake accounts, semi-supervised models can
exploit huge quantities of unlabeled data to increase performance. We purchased
and studied 1293 CT profiles from 11 providers to build our self-training
classifier, which reached 95\% F1-score. We tested our model in the wild by
detecting and analyzing CT engagement from 20 mega-influencers (i.e., with more
than one million followers), and discovered that more than 20% was artificial.
We analyzed the CT profiles and comments, showing that it is difficult to
detect these activities based solely on their generated content.
