Sexism Discovery using CNN, Word Embeddings, NLP and Data Augmentation

The pervasive issue of online sexism continues to pose significant challenges, fostering environments characterized by toxicity and perpetuating harmful societal norms. In response, this paper presents an approach for the discovery of sexist statements employing convolutional neural networks (CNNs),...

Full description

Saved in:
Bibliographic Details
Published in2024 10th International Conference on Control, Decision and Information Technologies (CoDIT) pp. 1685 - 1690
Main Authors Fattahi, Jaouhar, Sghaier, Feriel, Mejri, Mohamed, Ghayoula, Ridha, Bahroun, Sahbi, Ziadia, Marwa
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.07.2024
Subjects
Adaptation models
Convolutional neural networks
Data augmentation
Data models
Natural language processing
Resilience
Semantics
Toxicology
Training
Training data
Online AccessGet full text

Cover

More Information
Summary:The pervasive issue of online sexism continues to pose significant challenges, fostering environments characterized by toxicity and perpetuating harmful societal norms. In response, this paper presents an approach for the discovery of sexist statements employing convolutional neural networks (CNNs), Word Embeddings, and data augmentation techniques. Through the fusion of CNNs' capacity for hierarchical feature extraction with the semantic representations afforded by Word Embeddings, our method achieves exemplary discrimination performance. Additionally, the incorporation of data augmentation enriches the training dataset, thereby augmenting model generalization and resilience. Empirical evaluation on a larger dataset of statements demonstrates the efficacy of our approach, surpassing many baseline approaches in terms of discovery accuracy, precision, recall and F1-score.
ISSN:2576-3555
DOI:10.1109/CoDIT62066.2024.10708284