Cargando…

GANBOT: a GAN-based framework for social bot detection

Nowadays, a massive number of people are involved in various social media. This fact enables organizations and institutions to more easily access their audiences across the globe. Some of them use social bots as an automatic entity to gain intangible access and influence on their users by faster con...

Descripción completa

Detalles Bibliográficos
Autores principales: Najari, Shaghayegh, Salehi, Mostafa, Farahbakhsh, Reza
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Vienna 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8590628/
https://www.ncbi.nlm.nih.gov/pubmed/34804252
http://dx.doi.org/10.1007/s13278-021-00800-9
_version_ 1784599021464059904
author Najari, Shaghayegh
Salehi, Mostafa
Farahbakhsh, Reza
author_facet Najari, Shaghayegh
Salehi, Mostafa
Farahbakhsh, Reza
author_sort Najari, Shaghayegh
collection PubMed
description Nowadays, a massive number of people are involved in various social media. This fact enables organizations and institutions to more easily access their audiences across the globe. Some of them use social bots as an automatic entity to gain intangible access and influence on their users by faster content propagation. Thereby, malicious social bots are populating more and more to fool humans with their unrealistic behavior and content. Hence, that’s necessary to distinguish these fake social accounts from real ones. Multiple approaches have been investigated in the literature to answer this problem. Statistical machine learning methods are one of them focusing on handcrafted features to represent characteristics of social bots. Although they reached successful results in some cases, they relied on the bot’s behavior and failed in the behavioral change patterns of bots. On the other hands, more advanced deep neural network-based methods aim to overcome this limitation. Generative adversarial network (GAN) as new technology from this domain is a semi-supervised method that demonstrates to extract the behavioral pattern of the data. In this work, we use GAN to leak more information of bot samples for state-of-the-art textual bot detection method (Contextual LSTM). Although GAN augments low labeled data, original textual GAN (Sequence Generative Adversarial Net (SeqGAN)) has the known limitation of convergence. In this paper, we invested this limitation and customized the GAN idea in a new framework called GANBOT, in which the generator and classifier connect by an LSTM layer as a shared channel between them. Our experimental results on a bench-marked dataset of Twitter social bot show our proposed framework outperforms the existing contextual LSTM method by increasing bot detection probabilities.
format Online
Article
Text
id pubmed-8590628
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher Springer Vienna
record_format MEDLINE/PubMed
spelling pubmed-85906282021-11-15 GANBOT: a GAN-based framework for social bot detection Najari, Shaghayegh Salehi, Mostafa Farahbakhsh, Reza Soc Netw Anal Min Original Article Nowadays, a massive number of people are involved in various social media. This fact enables organizations and institutions to more easily access their audiences across the globe. Some of them use social bots as an automatic entity to gain intangible access and influence on their users by faster content propagation. Thereby, malicious social bots are populating more and more to fool humans with their unrealistic behavior and content. Hence, that’s necessary to distinguish these fake social accounts from real ones. Multiple approaches have been investigated in the literature to answer this problem. Statistical machine learning methods are one of them focusing on handcrafted features to represent characteristics of social bots. Although they reached successful results in some cases, they relied on the bot’s behavior and failed in the behavioral change patterns of bots. On the other hands, more advanced deep neural network-based methods aim to overcome this limitation. Generative adversarial network (GAN) as new technology from this domain is a semi-supervised method that demonstrates to extract the behavioral pattern of the data. In this work, we use GAN to leak more information of bot samples for state-of-the-art textual bot detection method (Contextual LSTM). Although GAN augments low labeled data, original textual GAN (Sequence Generative Adversarial Net (SeqGAN)) has the known limitation of convergence. In this paper, we invested this limitation and customized the GAN idea in a new framework called GANBOT, in which the generator and classifier connect by an LSTM layer as a shared channel between them. Our experimental results on a bench-marked dataset of Twitter social bot show our proposed framework outperforms the existing contextual LSTM method by increasing bot detection probabilities. Springer Vienna 2021-11-14 2022 /pmc/articles/PMC8590628/ /pubmed/34804252 http://dx.doi.org/10.1007/s13278-021-00800-9 Text en © The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature 2021 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Original Article
Najari, Shaghayegh
Salehi, Mostafa
Farahbakhsh, Reza
GANBOT: a GAN-based framework for social bot detection
title GANBOT: a GAN-based framework for social bot detection
title_full GANBOT: a GAN-based framework for social bot detection
title_fullStr GANBOT: a GAN-based framework for social bot detection
title_full_unstemmed GANBOT: a GAN-based framework for social bot detection
title_short GANBOT: a GAN-based framework for social bot detection
title_sort ganbot: a gan-based framework for social bot detection
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8590628/
https://www.ncbi.nlm.nih.gov/pubmed/34804252
http://dx.doi.org/10.1007/s13278-021-00800-9
work_keys_str_mv AT najarishaghayegh ganbotaganbasedframeworkforsocialbotdetection
AT salehimostafa ganbotaganbasedframeworkforsocialbotdetection
AT farahbakhshreza ganbotaganbasedframeworkforsocialbotdetection