Internet overuse is a widespread phenomenon in today's digital society. Existing interventions, such as time limits or grayscaling, often rely on restrictive controls that provoke psychological reactance and are frequently circumvented. Building on prior work showing that emotional responses mediate the relationship between content consumption and online engagement, we investigate whether regulating the emotional impact of images can reduce online use in a non-coercive manner. We introduce and systematically analyze three regressor-guided image-editing approaches: (i) global optimization of emotion-related image attributes, (ii) optimization in a style latent space, and (iii) a diffusion-based method using classifier and classifier-free guidance. While the first two approaches modify low-level visual features (e.g., contrast, color), the diffusion-based method enables higher-level changes (e.g., adjusting clothing, facial features). Results from a controlled image-rating study and a social media experiment show that diffusion-based edits balance emotional responses and are associated with lower usage duration while preserving visual quality.

翻译：网络过度使用是当今数字社会中普遍存在的现象。现有干预措施（如时间限制或灰度显示）通常依赖于限制性控制，容易引发心理抗拒并常被规避。基于先前研究表明情绪反应在内容消费与在线参与之间起中介作用，我们探究了调节图像情感影响能否以非强制方式减少在线使用。我们引入并系统分析了三种回归器引导的图像编辑方法：（i）情感相关图像属性的全局优化，（ii）风格潜空间中的优化，以及（iii）基于分类器与无分类器引导的扩散方法。前两种方法主要修改低级视觉特征（如对比度、色彩），而基于扩散的方法能实现更高级别的调整（如调整服饰、面部特征）。受控图像评分研究及社交媒体实验结果表明，基于扩散的编辑能在保持视觉质量的同时平衡情绪反应，并与更短的使用时长相关。