  The growing use of social 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 MLand 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 paper, 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, namely (i) rumors detection, (ii) satires detection,
(iii) clickbait & spams identification, (iv) hate speech detection,
(v)misinformation detection, and (vi) sentiment analysis. We then highlight the
concurrent and anticipated future research questions and provide
recommendations and directions for future work.
