  Impersonators are playing an important role in the production and propagation
of the content on Online Social Networks, notably on Instagram. These entities
are nefarious fake accounts that intend to disguise a legitimate account by
making similar profiles and then striking social media by fake content, which
makes it considerably harder to understand which posts are genuinely produced.
In this study, we focus on three important communities with legitimate verified
accounts. Among them, we identify a collection of 2.2K impersonator profiles
with nearly 10k generated posts, 68K comments, and 90K likes. Then, based on
profile characteristics and user behaviours, we cluster them into two
collections of `bot' and `fan'. In order to separate the impersonator-generated
post from genuine content, we propose a Deep Neural Network architecture that
measures `profiles' and `posts' features to predict the content type:
`bot-generated', 'fan-generated', or `genuine' content. Our study shed light
into this interesting phenomena and provides interesting observation on
bot-generated content that can help us to understand the role of impersonators
in the production of fake content on Instagram.
