Detecting Chinese Sexism Text in Social Media Using Hybrid Deep Learning Model with Sarcasm Masking
Sexism content is prevalent in social media, which seriously affects the online environment and occasionally leads to offline disputes. For this reason, many scholars have researched how to automatically detect sexist content in social media. However, the presence of sarcasm complicates this task. T...
| Published in: | INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS |
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| Main Authors: | , , , |
| Format: | Article |
| Language: | English |
| Published: |
SCIENCE & INFORMATION SAI ORGANIZATION LTD
2025
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| Subjects: | |
| Online Access: | https://www-webofscience-com.uitm.idm.oclc.org/wos/woscc/full-record/WOS:001441789600001 |
| Summary: | Sexism content is prevalent in social media, which seriously affects the online environment and occasionally leads to offline disputes. For this reason, many scholars have researched how to automatically detect sexist content in social media. However, the presence of sarcasm complicates this task. Thus, recognizing sarcasm to improve the accuracy of sexism detection has become a crucial research focus. In this study, we adopt a deep learning approach by combining a sexism lexicon and a sarcasm lexicon to work on the detection of Chinese sexist content in social media. We innovatively propose a sarcasm- based masking mechanism, which achieves an accuracy of 82.65% and a macro F1 score of 80.49% on the Sina Weibo Sexism Review (SWSR) dataset, significantly outperforming the baseline model by 2.05% and 2.89%, respectively. This study combines the irony masking mechanism with sexism detection, and the experimental results demonstrate the effectiveness of the deep learning method based on the irony masking mechanism in Chinese sexism detection. |
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| ISSN: | 2158-107X 2156-5570 |