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

Koosha Zarei; Reza Farahbakhsh; Noel Crespi; Gareth Tyson

doi:10.1109/ASONAM49781.2020.9381437

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

Koosha Zarei
, Reza Farahbakhsh
, Noel Crespi
, Gareth Tyson

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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.

Original language	English
Title of host publication	Proceedings of the 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2020
Editors	Martin Atzmuller, Michele Coscia, Rokia Missaoui
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	11-15
Number of pages	5
ISBN (Electronic)	9781728110561
DOIs	https://doi.org/10.1109/ASONAM49781.2020.9381437
Publication status	Published - 7 Dec 2020
Event	12th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2020 - Virtual, Online, Netherlands Duration: 7 Dec 2020 → 10 Dec 2020

Publication series

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

Conference

Conference	12th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2020
Country/Territory	Netherlands
City	Virtual, Online
Period	7/12/20 → 10/12/20

Keywords

Bot
Fake Content
Fake Engagement
Fake Profile
Impersonators
Instagram
Social Media

Access to Document

10.1109/ASONAM49781.2020.9381437

Cite this

Zarei, K., Farahbakhsh, R., Crespi, N., & Tyson, G. (2020). Impersonation on Social Media: A Deep Neural Approach to Identify Ingenuine Content. In M. Atzmuller, M. Coscia, & R. Missaoui (Eds.), Proceedings of the 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2020 (pp. 11-15). Article 9381437 (Proceedings of the 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2020). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ASONAM49781.2020.9381437

Zarei, Koosha ; Farahbakhsh, Reza ; Crespi, Noel et al. / Impersonation on Social Media : A Deep Neural Approach to Identify Ingenuine Content. Proceedings of the 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2020. editor / Martin Atzmuller ; Michele Coscia ; Rokia Missaoui. Institute of Electrical and Electronics Engineers Inc., 2020. pp. 11-15 (Proceedings of the 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2020).

@inproceedings{ea5cdb277f5049c995ff1687f02ca8b7,

title = "Impersonation on Social Media: A Deep Neural Approach to Identify Ingenuine Content",

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.",

keywords = "Bot, Fake Content, Fake Engagement, Fake Profile, Impersonators, Instagram, Social Media",

author = "Koosha Zarei and Reza Farahbakhsh and Noel Crespi and Gareth Tyson",

note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 12th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2020 ; Conference date: 07-12-2020 Through 10-12-2020",

year = "2020",

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doi = "10.1109/ASONAM49781.2020.9381437",

language = "English",

series = "Proceedings of the 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2020",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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booktitle = "Proceedings of the 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2020",

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Zarei, K, Farahbakhsh, R, Crespi, N & Tyson, G 2020, Impersonation on Social Media: A Deep Neural Approach to Identify Ingenuine Content. in M Atzmuller, M Coscia & R Missaoui (eds), Proceedings of the 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2020., 9381437, Proceedings of the 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2020, Institute of Electrical and Electronics Engineers Inc., pp. 11-15, 12th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2020, Virtual, Online, Netherlands, 7/12/20. https://doi.org/10.1109/ASONAM49781.2020.9381437

Impersonation on Social Media: A Deep Neural Approach to Identify Ingenuine Content. / Zarei, Koosha; Farahbakhsh, Reza; Crespi, Noel et al.
Proceedings of the 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2020. ed. / Martin Atzmuller; Michele Coscia; Rokia Missaoui. Institute of Electrical and Electronics Engineers Inc., 2020. p. 11-15 9381437 (Proceedings of the 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2020).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Impersonation on Social Media

T2 - 12th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2020

AU - Zarei, Koosha

AU - Farahbakhsh, Reza

AU - Crespi, Noel

AU - Tyson, Gareth

PY - 2020/12/7

Y1 - 2020/12/7

N2 - 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.

AB - 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.

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KW - Fake Engagement

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KW - Instagram

KW - Social Media

U2 - 10.1109/ASONAM49781.2020.9381437

DO - 10.1109/ASONAM49781.2020.9381437

M3 - Conference contribution

AN - SCOPUS:85103696407

T3 - Proceedings of the 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2020

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

A2 - Atzmuller, Martin

A2 - Coscia, Michele

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PB - Institute of Electrical and Electronics Engineers Inc.

Y2 - 7 December 2020 through 10 December 2020

ER -

Zarei K, Farahbakhsh R, Crespi N, Tyson G. Impersonation on Social Media: A Deep Neural Approach to Identify Ingenuine Content. In Atzmuller M, Coscia M, Missaoui R, editors, Proceedings of the 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2020. Institute of Electrical and Electronics Engineers Inc. 2020. p. 11-15. 9381437. (Proceedings of the 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2020). doi: 10.1109/ASONAM49781.2020.9381437

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

Abstract

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Keywords

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