Exploring Automatic Hate Speech Detection on Social Media: A Focus on Content-Based Analysis

Francimaria R. S. Nascimento, George D. C. Cavalcanti and Márjory Da Costa-Abreu

SAGE Open, 2023, vol. 13, issue 2, 21582440231181311

Abstract: Hate speech is a challenging problem, and its dissemination can cause potential harm to individuals and society by creating a sense of general unwelcoming to the marginalized groups, which usually are targeted. Therefore, it is essential to understand this issue and which techniques are useful for automatic detection. This paper presents a survey on automatic hate speech detection on social media, providing a structured overview of theoretical aspects and practical resources. Thus, we review different definitions of the term “hate speech†from social network platforms and the scientific community. We also present an overview of the methodologies used for hate speech detection, and we describe the main approaches currently explored in this context, including popular features, datasets, and algorithms. Furthermore, we discuss some challenges and opportunities for better solving this issue.

Keywords: hate speech detection; social media; survey; metadata; text features; natural language processing (search for similar items in EconPapers)
Date: 2023
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://journals.sagepub.com/doi/10.1177/21582440231181311 (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:sae:sagope:v:13:y:2023:i:2:p:21582440231181311

DOI: 10.1177/21582440231181311

Access Statistics for this article

More articles in SAGE Open
Bibliographic data for series maintained by SAGE Publications ().