A retrieval-augmented prompting network for hateful meme detection
The rise of user-generated content on social media is making memes a prevalent medium for expression. However, some memes convey offensive information toward individuals or groups on particular aspects. Detecting such harmful content is essential to mitigate potential conflicts and harm. This paper...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-07-01
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| Series: | Frontiers in Physics |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fphy.2025.1614267/full |
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| author | Qiuhua Kuang Yihao Lin Junxi Liu Xiazhi Lai Runlu Zhong |
| author_facet | Qiuhua Kuang Yihao Lin Junxi Liu Xiazhi Lai Runlu Zhong |
| author_sort | Qiuhua Kuang |
| collection | DOAJ |
| description | The rise of user-generated content on social media is making memes a prevalent medium for expression. However, some memes convey offensive information toward individuals or groups on particular aspects. Detecting such harmful content is essential to mitigate potential conflicts and harm. This paper proposes a retrieval-augmented prompting network (RAPN) for hateful meme detection. The proposed model utilizes a retrieval-augmented selector to identify semantically relevant prompting examples from diverse sources, enhancing the selection to better match the inference instances. Based on the prompting framework, attention networks are employed to extract critical features from input instance and examples. By applying contrastive learning to label and feature spaces, the model is capable of learning more discriminative information for classification. Comprehensive evaluations on benchmark datasets demonstrate that our model outperforms the baseline methods. Thereby, the proposed model has strong evidence of high accuracy on the task of hateful meme classification. |
| format | Article |
| id | doaj-art-897ff8096d7f43569e12cc12b140b16a |
| institution | Kabale University |
| issn | 2296-424X |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Physics |
| spelling | doaj-art-897ff8096d7f43569e12cc12b140b16a2025-08-20T03:29:40ZengFrontiers Media S.A.Frontiers in Physics2296-424X2025-07-011310.3389/fphy.2025.16142671614267A retrieval-augmented prompting network for hateful meme detectionQiuhua Kuang0Yihao Lin1Junxi Liu2Xiazhi Lai3Runlu Zhong4School of Computer Science, Guangdong University of Education, Guangzhou, ChinaSchool of Electronic Science and Engineering, South China Normal University, Foshan, ChinaSchool of Electronic Science and Engineering, South China Normal University, Foshan, ChinaSchool of Computer Science, Guangdong University of Education, Guangzhou, ChinaSchool of Information Engineering, Guangzhou Panyu Polytechnic, Guangzhou, ChinaThe rise of user-generated content on social media is making memes a prevalent medium for expression. However, some memes convey offensive information toward individuals or groups on particular aspects. Detecting such harmful content is essential to mitigate potential conflicts and harm. This paper proposes a retrieval-augmented prompting network (RAPN) for hateful meme detection. The proposed model utilizes a retrieval-augmented selector to identify semantically relevant prompting examples from diverse sources, enhancing the selection to better match the inference instances. Based on the prompting framework, attention networks are employed to extract critical features from input instance and examples. By applying contrastive learning to label and feature spaces, the model is capable of learning more discriminative information for classification. Comprehensive evaluations on benchmark datasets demonstrate that our model outperforms the baseline methods. Thereby, the proposed model has strong evidence of high accuracy on the task of hateful meme classification.https://www.frontiersin.org/articles/10.3389/fphy.2025.1614267/fullhateful meme detectionpromptretrieval-augmented strategyattention mechanismcontrastive learning |
| spellingShingle | Qiuhua Kuang Yihao Lin Junxi Liu Xiazhi Lai Runlu Zhong A retrieval-augmented prompting network for hateful meme detection Frontiers in Physics hateful meme detection prompt retrieval-augmented strategy attention mechanism contrastive learning |
| title | A retrieval-augmented prompting network for hateful meme detection |
| title_full | A retrieval-augmented prompting network for hateful meme detection |
| title_fullStr | A retrieval-augmented prompting network for hateful meme detection |
| title_full_unstemmed | A retrieval-augmented prompting network for hateful meme detection |
| title_short | A retrieval-augmented prompting network for hateful meme detection |
| title_sort | retrieval augmented prompting network for hateful meme detection |
| topic | hateful meme detection prompt retrieval-augmented strategy attention mechanism contrastive learning |
| url | https://www.frontiersin.org/articles/10.3389/fphy.2025.1614267/full |
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