Adversarial Machine Learning on Social Network: A Survey

In recent years, machine learning technology has made great improvements in social networks applications such as social network recommendation systems, sentiment analysis, and text generation. However, it cannot be ignored that machine learning algorithms are vulnerable to adversarial examples, that...

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Autores principales:	Sensen Guo, Xiaoyu Li, Zhiying Mu
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	social networks adversarial examples sentiment analysis recommendation system spam detection Physics QC1-999
Acceso en línea:	https://doaj.org/article/a6251993374241b295171fc2e807fc5a
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spelling	oai:doaj.org-article:a6251993374241b295171fc2e807fc5a2021-12-01T10:35:25ZAdversarial Machine Learning on Social Network: A Survey2296-424X10.3389/fphy.2021.766540https://doaj.org/article/a6251993374241b295171fc2e807fc5a2021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fphy.2021.766540/fullhttps://doaj.org/toc/2296-424XIn recent years, machine learning technology has made great improvements in social networks applications such as social network recommendation systems, sentiment analysis, and text generation. However, it cannot be ignored that machine learning algorithms are vulnerable to adversarial examples, that is, adding perturbations that are imperceptible to the human eye to the original data can cause machine learning algorithms to make wrong outputs with high probability. This also restricts the widespread use of machine learning algorithms in real life. In this paper, we focus on adversarial machine learning algorithms on social networks in recent years from three aspects: sentiment analysis, recommendation system, and spam detection, We review some typical applications of machine learning algorithms and adversarial example generation and defense algorithms for machine learning algorithms in the above three aspects in recent years. besides, we also analyze the current research progress and prospects for the directions of future research.Sensen GuoSensen GuoXiaoyu LiXiaoyu LiZhiying MuZhiying MuFrontiers Media S.A.articlesocial networksadversarial examplessentiment analysisrecommendation systemspam detectionPhysicsQC1-999ENFrontiers in Physics, Vol 9 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	social networks adversarial examples sentiment analysis recommendation system spam detection Physics QC1-999
spellingShingle	social networks adversarial examples sentiment analysis recommendation system spam detection Physics QC1-999 Sensen Guo Sensen Guo Xiaoyu Li Xiaoyu Li Zhiying Mu Zhiying Mu Adversarial Machine Learning on Social Network: A Survey
description	In recent years, machine learning technology has made great improvements in social networks applications such as social network recommendation systems, sentiment analysis, and text generation. However, it cannot be ignored that machine learning algorithms are vulnerable to adversarial examples, that is, adding perturbations that are imperceptible to the human eye to the original data can cause machine learning algorithms to make wrong outputs with high probability. This also restricts the widespread use of machine learning algorithms in real life. In this paper, we focus on adversarial machine learning algorithms on social networks in recent years from three aspects: sentiment analysis, recommendation system, and spam detection, We review some typical applications of machine learning algorithms and adversarial example generation and defense algorithms for machine learning algorithms in the above three aspects in recent years. besides, we also analyze the current research progress and prospects for the directions of future research.
format	article
author	Sensen Guo Sensen Guo Xiaoyu Li Xiaoyu Li Zhiying Mu Zhiying Mu
author_facet	Sensen Guo Sensen Guo Xiaoyu Li Xiaoyu Li Zhiying Mu Zhiying Mu
author_sort	Sensen Guo
title	Adversarial Machine Learning on Social Network: A Survey
title_short	Adversarial Machine Learning on Social Network: A Survey
title_full	Adversarial Machine Learning on Social Network: A Survey
title_fullStr	Adversarial Machine Learning on Social Network: A Survey
title_full_unstemmed	Adversarial Machine Learning on Social Network: A Survey
title_sort	adversarial machine learning on social network: a survey
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/a6251993374241b295171fc2e807fc5a
work_keys_str_mv	AT sensenguo adversarialmachinelearningonsocialnetworkasurvey AT sensenguo adversarialmachinelearningonsocialnetworkasurvey AT xiaoyuli adversarialmachinelearningonsocialnetworkasurvey AT xiaoyuli adversarialmachinelearningonsocialnetworkasurvey AT zhiyingmu adversarialmachinelearningonsocialnetworkasurvey AT zhiyingmu adversarialmachinelearningonsocialnetworkasurvey
_version_	1718405315846733824

Adversarial Machine Learning on Social Network: A Survey

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