Designed to Spread: A Generative Approach to Enhance Information Diffusion
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Acceso en línea: | |
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| _version_ | 1866911507895287808 |
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| author | Qian, Ziqing Lei, Jiaying Dang, Shengqi Cao, Nan |
| author_facet | Qian, Ziqing Lei, Jiaying Dang, Shengqi Cao, Nan |
| contents | Social media has fundamentally transformed how people access information and form social connections, with content expression playing a critical role in driving information diffusion. While prior research has focused largely on network structures and tipping point identification, it provides limited tools for automatically generating content tailored for virality within a specific audience. To fill this gap, we propose the novel task of DOCG and introduce an information enhancement algorithm for generating content optimized for diffusion. Our method includes an influence indicator that enables content-level diffusion assessment without requiring access to network topology, and an information editor that employs reinforcement learning to explore interpretable editing strategies. The editor leverages generative models to produce semantically faithful, audience-aware textual or visual content. Experiments on real-world social media datasets and user study demonstrate that our approach significantly improves diffusion effectiveness while preserving the core semantics of the original content. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_12516 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Designed to Spread: A Generative Approach to Enhance Information Diffusion Qian, Ziqing Lei, Jiaying Dang, Shengqi Cao, Nan Social and Information Networks Social media has fundamentally transformed how people access information and form social connections, with content expression playing a critical role in driving information diffusion. While prior research has focused largely on network structures and tipping point identification, it provides limited tools for automatically generating content tailored for virality within a specific audience. To fill this gap, we propose the novel task of DOCG and introduce an information enhancement algorithm for generating content optimized for diffusion. Our method includes an influence indicator that enables content-level diffusion assessment without requiring access to network topology, and an information editor that employs reinforcement learning to explore interpretable editing strategies. The editor leverages generative models to produce semantically faithful, audience-aware textual or visual content. Experiments on real-world social media datasets and user study demonstrate that our approach significantly improves diffusion effectiveness while preserving the core semantics of the original content. |
| title | Designed to Spread: A Generative Approach to Enhance Information Diffusion |
| topic | Social and Information Networks |
| url | https://arxiv.org/abs/2511.12516 |