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

  • Koosha Zarei
  • , Reza Farahbakhsh
  • , Noel Crespi
  • , Gareth Tyson
  • Institut Polytechnique de Paris
  • Queen Mary University of London

Résultats de recherche: Le chapitre dans un livre, un rapport, une anthologie ou une collection › Contribution à une conférence › Revue par des pairs

16 Citations (Scopus)

Résumé

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.

langue originaleAnglais
titreProceedings of the 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2020
rédacteurs en chefMartin Atzmuller, Michele Coscia, Rokia Missaoui
EditeurInstitute of Electrical and Electronics Engineers Inc.
Pages11-15
Nombre de pages5
ISBN (Electronique)9781728110561
Les DOIs
étatPublié - 7 déc. 2020
Evénement12th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2020 - Virtual, Online, Pays-Bas
Durée: 7 déc. 2020 → 10 déc. 2020

Série de publications

NomProceedings of the 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2020

Une conférence

Une conférence12th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2020
Pays/TerritoirePays-Bas
La villeVirtual, Online
période7/12/20 → 10/12/20

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