Enhancing the Detection of Misogynistic Content in Social Media by Transferring Knowledge From Song Phrases
Misogyny is a serious social problem that affects the mental and physical health of women and can even lead to femicide. This problem is visible and prevalent in different communication channels, such as music and social networks, encouraging and reinforcing this harmful behavior. Given this situati...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10040492/ |
| _version_ | 1811163515731312640 |
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| author | Ricardo Calderon-Suarez Rosa M. Ortega-Mendoza Manuel Montes-Y-Gomez Carina Toxqui-Quitl Marco A. Marquez-Vera |
| author_facet | Ricardo Calderon-Suarez Rosa M. Ortega-Mendoza Manuel Montes-Y-Gomez Carina Toxqui-Quitl Marco A. Marquez-Vera |
| author_sort | Ricardo Calderon-Suarez |
| collection | DOAJ |
| description | Misogyny is a serious social problem that affects the mental and physical health of women and can even lead to femicide. This problem is visible and prevalent in different communication channels, such as music and social networks, encouraging and reinforcing this harmful behavior. Given this situation, the automatic detection of misogynistic content on social networks is a task of increasing interest. In this regard, most current computational approaches employ a supervised machine learning strategy. The main challenge is to capture the diversity and complexity of offensive language directed at women. Accordingly, the size and quality of training data play a fundamental role in the results of the methods. In this paper, we propose a novel data augmentation approach that takes advantage of song lyrics to increase the generalization capability of methods and improve their performance. Hence, we present a methodology for automatically compiling a corpus of song phrases that show abusive and explicit words against women. The proposed approach was evaluated using English and Spanish benchmark datasets, obtaining results that outperform conventional transfer learning techniques and achieve high competitiveness compared with state-of-the-art methods. |
| first_indexed | 2024-04-10T15:07:32Z |
| format | Article |
| id | doaj.art-79c762e6fe7a427e9ad4fb7b25a87645 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-10T15:07:32Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-79c762e6fe7a427e9ad4fb7b25a876452023-02-15T00:00:49ZengIEEEIEEE Access2169-35362023-01-0111131791319010.1109/ACCESS.2023.324296510040492Enhancing the Detection of Misogynistic Content in Social Media by Transferring Knowledge From Song PhrasesRicardo Calderon-Suarez0Rosa M. Ortega-Mendoza1https://orcid.org/0000-0002-3591-6567Manuel Montes-Y-Gomez2Carina Toxqui-Quitl3Marco A. Marquez-Vera4División de Investigación y Posgrado, Universidad Politécnica de Tulancingo (UPT), Tulancingo, Hidalgo, MexicoDivisión de Investigación y Posgrado, Universidad Politécnica de Tulancingo (UPT), Tulancingo, Hidalgo, MexicoCoordinación de Ciencias Computacionales, Instituto Nacional de Astrofísica, Óptica y Electrónica (INAOE), Puebla, MexicoDivisión de Investigación y Posgrado, Universidad Politécnica de Tulancingo (UPT), Tulancingo, Hidalgo, MexicoDepartamento de Mecatrónica, Universidad Politécnica de Pachuca (UPP), Zempoala, Hidalgo, MexicoMisogyny is a serious social problem that affects the mental and physical health of women and can even lead to femicide. This problem is visible and prevalent in different communication channels, such as music and social networks, encouraging and reinforcing this harmful behavior. Given this situation, the automatic detection of misogynistic content on social networks is a task of increasing interest. In this regard, most current computational approaches employ a supervised machine learning strategy. The main challenge is to capture the diversity and complexity of offensive language directed at women. Accordingly, the size and quality of training data play a fundamental role in the results of the methods. In this paper, we propose a novel data augmentation approach that takes advantage of song lyrics to increase the generalization capability of methods and improve their performance. Hence, we present a methodology for automatically compiling a corpus of song phrases that show abusive and explicit words against women. The proposed approach was evaluated using English and Spanish benchmark datasets, obtaining results that outperform conventional transfer learning techniques and achieve high competitiveness compared with state-of-the-art methods.https://ieeexplore.ieee.org/document/10040492/Data augmentationmisogyny detectiontransfer learningsocial mediasong lyrics |
| spellingShingle | Ricardo Calderon-Suarez Rosa M. Ortega-Mendoza Manuel Montes-Y-Gomez Carina Toxqui-Quitl Marco A. Marquez-Vera Enhancing the Detection of Misogynistic Content in Social Media by Transferring Knowledge From Song Phrases IEEE Access Data augmentation misogyny detection transfer learning social media song lyrics |
| title | Enhancing the Detection of Misogynistic Content in Social Media by Transferring Knowledge From Song Phrases |
| title_full | Enhancing the Detection of Misogynistic Content in Social Media by Transferring Knowledge From Song Phrases |
| title_fullStr | Enhancing the Detection of Misogynistic Content in Social Media by Transferring Knowledge From Song Phrases |
| title_full_unstemmed | Enhancing the Detection of Misogynistic Content in Social Media by Transferring Knowledge From Song Phrases |
| title_short | Enhancing the Detection of Misogynistic Content in Social Media by Transferring Knowledge From Song Phrases |
| title_sort | enhancing the detection of misogynistic content in social media by transferring knowledge from song phrases |
| topic | Data augmentation misogyny detection transfer learning social media song lyrics |
| url | https://ieeexplore.ieee.org/document/10040492/ |
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