"Adversarial NLP for Social Network Applications: Attacks, Defenses, an" by Izzat Alsmadi, Kashif Ahmad et al.
 

Adversarial NLP for Social Network Applications: Attacks, Defenses, and Research Directions

Document Type

Article

Publication Date

12-1-2023

Abstract

The growing use of media has led to the development of several machine learning (ML) and natural language processing (NLP) tools to process the unprecedented amount of social media content to make actionable decisions. However, these ML and NLP algorithms have been widely shown to be vulnerable to adversarial attacks. These vulnerabilities allow adversaries to launch a diversified set of adversarial attacks on these algorithms in different applications of social media text processing. In this article, we provide a comprehensive review of the main approaches for adversarial attacks and defenses in the context of social media applications with a particular focus on key challenges and future research directions. In detail, we cover literature on six key applications: 1) rumors detection; 2) satires detection; 3) clickbaits and spams identification; 4) hate speech detection; 5) misinformation detection; and 6) sentiment analysis. We then highlight the concurrent and anticipated future research questions and provide recommendations and directions for future work.

Identifier

85141613842 (Scopus)

Publication Title

IEEE Transactions on Computational Social Systems

External Full Text Location

https://doi.org/10.1109/TCSS.2022.3218743

e-ISSN

2329924X

First Page

3089

Last Page

3108

Issue

6

Volume

10

Grant

1120

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