Study of violence against women and its characteristics through the application of text mining techniques

Stephanie, E. M. A.; Ruiz, L. G. B.; Vila, M. A.; Pegalajar, M. C.

doi:10.1007/s41060-023-00448-y

Int J Data Sci Anal

2023

DOI: 10.1007/s41060-023-00448-y

|View full text |Cite

Study of violence against women and its characteristics through the application of text mining techniques

E. M. A. Stephanie,

L. G. B. Ruiz,

M. A. Vila

et al.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

A Systematic Literature Review on Automatic Sexism Detection in Social Media

Lei,

Abdullah,

Syed Aris

2024

Eng. Technol. Appl. Sci. Res.

View full text Add to dashboard Cite

Sexist content has become increasingly prevalent on social media platforms, underscoring the critical need for the development of efficient Automatic Sexism Detection methods. Previous literature reviews have not encompassed the new advancements in Automatic Sexism Detection observed over the past three years. Hence, the present study conducted a Systematic Literature Review (SLR) that examined 48 primary studies published between 2014 and 17th Sept. 2024, retrieved from six bibliographic databases. This paper aims to present a comprehensive literature review on Automatic Sexism Detection, encompassing the datasets, preprocessing techniques, feature extraction methods, text representations, classification approaches, and evaluation models employed in Automatic Sexism Detection research. The paper includes a discussion of the findings, limitations, and future research directions of the chosen articles. Additionally, it provides an overview of the conclusions drawn from the conducted research. The performed analysis reveals a lack of corpus beyond the English and Spanish language encountered in datasets, with most of the latter being annotated for either misogyny or non-misogyny. Common preprocessing techniques analyzed in the current study include lowercase conversion, text removal, tokenization, stemming, and rewriting. Discrete representations, such as TF-IDF, N-grams, and BoW, are frequently utilized, while distributed representations, like Bert and GloVe, are prominent. Bert is the predominant classification model utilized while combining lexical features can enhance the results in the majority of the discussed scenarios. Accuracy (A) and F1 score (F1) are the most widely deployed evaluation metrics in this field.

show abstract

A Systematic Literature Review on Automatic Sexism Detection in Social Media

Lei,

Abdullah,

Syed Aris

2024

Eng. Technol. Appl. Sci. Res.

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Study of violence against women and its characteristics through the application of text mining techniques

Cited by 1 publication

References 34 publications

A Systematic Literature Review on Automatic Sexism Detection in Social Media

A Systematic Literature Review on Automatic Sexism Detection in Social Media

Contact Info

Product

Resources

About