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

TL;DR

This paper presents a deep neural network approach to distinguish genuine Instagram posts from impersonator-generated content, focusing on profile and user behavior analysis to identify fake accounts and their posts.

Contribution

It introduces a novel deep learning architecture that classifies content as bot-generated, fan-generated, or genuine, based on profile and post features, addressing impersonation challenges.

Findings

Identified 2.2K impersonator profiles with extensive activity

Clustered impersonators into bot and fan categories

Achieved effective classification of content types

Abstract

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.