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
Recommended Citation
Alsmadi, Izzat; Ahmad, Kashif; Nazzal, Mahmoud; Alam, Firoj; Al-Fuqaha, Ala; Khreishah, Abdallah; and Algosaibi, Abdulelah, "Adversarial NLP for Social Network Applications: Attacks, Defenses, and Research Directions" (2023). Faculty Publications. 1299.
https://digitalcommons.njit.edu/fac_pubs/1299