MDPI - Publisher of Open Access Journals

24 pages, 1610 KB

Open AccessArticle

Leveraging Advanced NLP Techniques and Data Augmentation to Enhance Online Misogyny Detection

by Alaa Mohasseb, Eslam Amer, Fatima Chiroma and Alessia Tranchese

Appl. Sci. 2025, 15(2), 856; https://doi.org/10.3390/app15020856 - 16 Jan 2025

Cited by 4 | Viewed by 2849

Online misogyny is a significant societal challenge that reinforces gender inequalities and discourages women from engaging fully in digital spaces. Traditional moderation methods often fail to address the dynamic and context-dependent nature of misogynistic language, making adaptive solutions essential. This study presents a framework that integrates advanced natural-language processing techniques with strategic data augmentation to improve the detection of misogynistic content. Key contributions include emoji decoding to interpret symbolic communication, contextual expansion using Sentence-Transformer models, and LDA-based topic modeling to enhance data richness and contextual understanding. The framework incorporates machine-learning, deep-learning, and Transformer-based models to handle complex and nuanced language. Performance analysis highlights the effectiveness of the selected models, and comparative results emphasize the transformative role of data augmentation. This augmentation significantly enhanced model robustness, improved generalization, and strengthened the detection of misogynistic content. Full article

(This article belongs to the Special Issue New Trends in Natural Language Processing)

► Show Figures

Figure 1

25 pages, 632 KB

Open AccessArticle

Detection of Hate Speech, Racism and Misogyny in Digital Social Networks: Colombian Case Study

by Luis Gabriel Moreno-Sandoval, Alexandra Pomares-Quimbaya, Sergio Andres Barbosa-Sierra and Liliana Maria Pantoja-Rojas

Big Data Cogn. Comput. 2024, 8(9), 113; https://doi.org/10.3390/bdcc8090113 - 6 Sep 2024

Cited by 2 | Viewed by 4854

Abstract

The growing popularity of social networking platforms worldwide has substantially increased the presence of offensive language on these platforms. To date, most of the systems developed to mitigate this challenge focus primarily on English content. However, this issue is a global concern, and therefore, other languages, such as Spanish, are involved. This article addresses the task of identifying hate speech, racism, and misogyny in Spanish within the Colombian context on social networks, and introduces a gold standard dataset specifically developed for this purpose. Indeed, the experiment compares the performance of TLM models from Deep Learning methods, such as BERT, Roberta, XLM, and BETO adjusted to the Colombian slang domain, then compares the best TLM model against a GPT, having a significant impact on achieving more accurate predictions in this task. Finally, this study provides a detailed understanding of the different components used in the system, including the architecture of the models and the selection of functions. The best results show that the BERT model achieves an accuracy of 83.6% for hate speech detection, while the GPT model achieves an accuracy of 90.8% for racism speech and 90.4% for misogyny detection. Full article

► Show Figures

Figure 1

15 pages, 528 KB

Open AccessArticle

A Language Model for Misogyny Detection in Latin American Spanish Driven by Multisource Feature Extraction and Transformers

by Edwin Aldana-Bobadilla, Alejandro Molina-Villegas, Yuridia Montelongo-Padilla, Ivan Lopez-Arevalo and Oscar S. Sordia

Appl. Sci. 2021, 11(21), 10467; https://doi.org/10.3390/app112110467 - 8 Nov 2021

Cited by 8 | Viewed by 4610

Abstract

Creating effective mechanisms to detect misogyny online automatically represents significant scientific and technological challenges. The complexity of recognizing misogyny through computer models lies in the fact that it is a subtle type of violence, it is not always explicitly aggressive, and it can even hide behind seemingly flattering words, jokes, parodies, and other expressions. Currently, it is even difficult to have an exact figure for the rate of misogynistic comments online because, unlike other types of violence, such as physical violence, these events are not registered by any statistical systems. This research contributes to the development of models for the automatic detection of misogynistic texts in Latin American Spanish and contributes to the design of data augmentation methodologies since the amount of data required for deep learning models is considerable. Full article

(This article belongs to the Special Issue Current Approaches and Applications in Natural Language Processing)

► Show Figures

Graphical abstract

9 pages, 520 KB

Open AccessProceeding Paper

AI-Based Misogyny Detection from Arabic Levantine Twitter Tweets

by Abdullah Y. Muaad, Hanumanthappa Jayappa Davanagere, Mugahed A. Al-antari, J. V. Bibal Benifa and Channabasava Chola

Comput. Sci. Math. Forum 2022, 2(1), 15; https://doi.org/10.3390/IOCA2021-10880 - 19 Sep 2021

Cited by 7 | Viewed by 3182

Abstract

Twitter is one of the social media platforms that is extensively used to share public opinions. Arabic text detection system (ATDS) is a challenging computational task in the field of Natural Language Processing (NLP) using Artificial Intelligence (AI)-based techniques. The detection of misogyny in Arabic text has received a lot of attention in recent years due to the racial and verbal violence against women on social media platforms. In this paper, an Arabic text recognition approach is presented for detecting misogyny from Arabic tweets. The proposed approach is evaluated using the Arabic Levantine Twitter Dataset for Misogynistic, and it gained recognition accuracies of 90.0% and 89.0% for binary and multi-class tasks, respectively. The proposed approach seems to be useful in providing practical smart solutions for detecting Arabic misogyny on social media. Full article

(This article belongs to the Proceedings of The 1st International Electronic Conference on Algorithms)

► Show Figures

Figure 1

Search Results (4)

Further Information

Guidelines

MDPI Initiatives

Follow MDPI

Saved Queries

Search Filter Reset All

Years

Feature Papers

Subjects

Journals

Article Types

Countries / Regions

Search Results (4)

Further Information

Guidelines

MDPI Initiatives

Follow MDPI