Emotions are known to mediate the relationship between users' content consumption and their online engagement, with heightened emotional intensity leading to increased engagement. Building on this insight, we propose three regressor-guided image editing approaches aimed at diminishing the emotional impact of images. These include (i) a parameter optimization approach based on global image transformations known to influence emotions, (ii) an optimization approach targeting the style latent space of a generative adversarial network, and (iii) a diffusion-based approach employing classifier guidance and classifier-free guidance. Our findings demonstrate that approaches can effectively alter the emotional properties of images while maintaining high visual quality. Optimization-based methods primarily adjust low-level properties like color hues and brightness, whereas the diffusion-based approach introduces semantic changes, such as altering appearance or facial expressions. Notably, results from a behavioral study reveal that only the diffusion-based approach successfully elicits changes in viewers' emotional responses while preserving high perceived image quality. In future work, we will investigate the impact of these image adaptations on internet user behavior.

本研究解决了情感如何影响用户内容消费与网络参与之间关系的问题，提出了三种回归指导的图像编辑方法以减弱图像的情感影响。这些方法不仅能有效改变图像的情感特征，保持高视觉质量，还通过扩散方法成功引发观众的情感反应变化，预示着对互联网用户行为的潜在影响。

回归指导的图像编辑调节情感反应以减少网络参与