从潜在影响到语言：面向扩散的受众易感特征内容生成
From Latent Influence to Language: Diffusion-Oriented Content Generation via Audience-Susceptible Features
AI总结：
针对现有面向扩散的内容生成方法难以转化扩散影响信号的问题，提出三阶段框架DOCG-AS，实验显示其在预测扩散影响上优于现有基线。
AI中文摘要：
社交媒体上多模态用户生成内容的快速增长，使得信息扩散成为广告商和品牌营销人员的关键因素。然而，手动定制内容以引起特定受众共鸣既费力又依赖经验。尽管近期生成模型在自动内容生成方面展现出良好能力，但现有的面向扩散的内容生成方法仍难以将数值型扩散影响信号有效转化为可操作的指导，该指导需捕捉潜在的受众易感特征并考虑异质受众兴趣。为解决这些挑战，我们提出DOCG-AS，一个面向扩散的内容生成三阶段框架。它首先在真实内容流形上执行隐式特征优化，以发现最优传播特征向量；接着，使用可学习解码器将该向量显式解码为自然语言描述的可解释受众易感特征，为内容生成提供指导；最后，利用从不同优化初始化得到的多组受众易感特征，将用户输入重写为最终的多模态内容。实验表明，DOCG-AS在预测扩散影响方面始终优于最先进的基线方法。
英文摘要：
The rapid growth of multimodal user-generated content on social media has made information diffusion a critical factor for advertisers and brand marketers. However, manually tailoring content to resonate with specific audiences is labor-intensive and heuristic-driven. While recent generative models offer promising capabilities for automatic content generation, existing approaches for diffusion-oriented content generation still struggle to effectively translate numeric diffusion influence signals into actionable guidance that captures latent audience susceptibility and accounts for heterogeneous audience interests. To address these challenges, we propose DOCG-AS, a three-stage framework for diffusion-oriented content generation. It first performs implicit feature optimization on the realistic content manifold to discover an optimal propagation feature vector. Then, it explicitly decodes this vector using a learnable decoder into interpretable audience-susceptible features described in natural language, providing guidance for content generation. Finally, it leverages multiple sets of audience-susceptible features obtained from different optimization initializations to rewrite the user's input into the final multimodal content. Experiments demonstrate that DOCG-AS consistently outperforms state-of-the-art baselines in terms of predicted diffusion influence.
