  Recent years have seen a new form of advertisement campaigns emerge: those
involving so-called social media influencers. These influencers accept money in
return for promoting products via their social media feeds. Although this
constitutes a new and interesting form of marketing, it also raises many
questions, particularly related to transparency and regulation. For example, it
can sometimes be unclear which accounts are officially influencers, or what
even constitutes an influencer/advert. This is important in order to establish
the integrity of influencers and to ensure compliance with advertisement
regulation. We gather a large-scale Instagram dataset covering thousands of
accounts advertising products, and create a categorisation based on the number
of users they reach. We then provide a detailed analysis of the types of
products being advertised by these accounts, their potential reach, and the
engagement they receive from their followers. Based on our findings, we train
machine learning models to distinguish sponsored content from non-sponsored,
and identify cases where people are generating sponsored posts without
officially labelling them. Our findings provide a first step towards
understanding the under-studied space of online influencers that could be
useful for researchers, marketers and policymakers.
