MemeCraft: Contextual and Stance-Driven Multimodal Meme Generation
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arXiv
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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866911808732790784 |
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| author | Wang, Han Lee, Roy Ka-Wei |
| author_facet | Wang, Han Lee, Roy Ka-Wei |
| contents | Online memes have emerged as powerful digital cultural artifacts in the age of social media, offering not only humor but also platforms for political discourse, social critique, and information dissemination. Their extensive reach and influence in shaping online communities' sentiments make them invaluable tools for campaigning and promoting ideologies. Despite the development of several meme-generation tools, there remains a gap in their systematic evaluation and their ability to effectively communicate ideologies. Addressing this, we introduce MemeCraft, an innovative meme generator that leverages large language models (LLMs) and visual language models (VLMs) to produce memes advocating specific social movements. MemeCraft presents an end-to-end pipeline, transforming user prompts into compelling multimodal memes without manual intervention. Conscious of the misuse potential in creating divisive content, an intrinsic safety mechanism is embedded to curb hateful meme production. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_14652 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | MemeCraft: Contextual and Stance-Driven Multimodal Meme Generation Wang, Han Lee, Roy Ka-Wei Computers and Society Artificial Intelligence Computation and Language Multimedia I.2.7; I.2.10 Online memes have emerged as powerful digital cultural artifacts in the age of social media, offering not only humor but also platforms for political discourse, social critique, and information dissemination. Their extensive reach and influence in shaping online communities' sentiments make them invaluable tools for campaigning and promoting ideologies. Despite the development of several meme-generation tools, there remains a gap in their systematic evaluation and their ability to effectively communicate ideologies. Addressing this, we introduce MemeCraft, an innovative meme generator that leverages large language models (LLMs) and visual language models (VLMs) to produce memes advocating specific social movements. MemeCraft presents an end-to-end pipeline, transforming user prompts into compelling multimodal memes without manual intervention. Conscious of the misuse potential in creating divisive content, an intrinsic safety mechanism is embedded to curb hateful meme production. |
| title | MemeCraft: Contextual and Stance-Driven Multimodal Meme Generation |
| topic | Computers and Society Artificial Intelligence Computation and Language Multimedia I.2.7; I.2.10 |
| url | https://arxiv.org/abs/2403.14652 |