Leveraging Advanced NLP Techniques and Data Augmentation to Enhance Online Misogyny Detection
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 essen...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/2/856 |
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| author | Alaa Mohasseb Eslam Amer Fatima Chiroma Alessia Tranchese |
| author_facet | Alaa Mohasseb Eslam Amer Fatima Chiroma Alessia Tranchese |
| author_sort | Alaa Mohasseb |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-1c19261d5a14428aa3324a21f2f3bcc9 |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-1c19261d5a14428aa3324a21f2f3bcc92025-01-24T13:21:04ZengMDPI AGApplied Sciences2076-34172025-01-0115285610.3390/app15020856Leveraging Advanced NLP Techniques and Data Augmentation to Enhance Online Misogyny DetectionAlaa Mohasseb0Eslam Amer1Fatima Chiroma2Alessia Tranchese3School of Computing, University of Portsmouth, Portsmouth PO1 2UP, UKSchool of Computing, University of Portsmouth, Portsmouth PO1 2UP, UKSchool of Computing, University of Portsmouth, Portsmouth PO1 2UP, UKSchool of Education, Languages and Linguistics, University of Portsmouth, Portsmouth PO1 2UP, UKOnline 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.https://www.mdpi.com/2076-3417/15/2/856online misogynydeep learningmachine learningtransformer modelspredication modelsNLP |
| spellingShingle | Alaa Mohasseb Eslam Amer Fatima Chiroma Alessia Tranchese Leveraging Advanced NLP Techniques and Data Augmentation to Enhance Online Misogyny Detection Applied Sciences online misogyny deep learning machine learning transformer models predication models NLP |
| title | Leveraging Advanced NLP Techniques and Data Augmentation to Enhance Online Misogyny Detection |
| title_full | Leveraging Advanced NLP Techniques and Data Augmentation to Enhance Online Misogyny Detection |
| title_fullStr | Leveraging Advanced NLP Techniques and Data Augmentation to Enhance Online Misogyny Detection |
| title_full_unstemmed | Leveraging Advanced NLP Techniques and Data Augmentation to Enhance Online Misogyny Detection |
| title_short | Leveraging Advanced NLP Techniques and Data Augmentation to Enhance Online Misogyny Detection |
| title_sort | leveraging advanced nlp techniques and data augmentation to enhance online misogyny detection |
| topic | online misogyny deep learning machine learning transformer models predication models NLP |
| url | https://www.mdpi.com/2076-3417/15/2/856 |
| work_keys_str_mv | AT alaamohasseb leveragingadvancednlptechniquesanddataaugmentationtoenhanceonlinemisogynydetection AT eslamamer leveragingadvancednlptechniquesanddataaugmentationtoenhanceonlinemisogynydetection AT fatimachiroma leveragingadvancednlptechniquesanddataaugmentationtoenhanceonlinemisogynydetection AT alessiatranchese leveragingadvancednlptechniquesanddataaugmentationtoenhanceonlinemisogynydetection |