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

Impersonation on Social Media: A Deep Neural Approach to Identify Ingenuine Content