用图像编辑调节情绪，让用户更早主动离开社交媒体。
Regressor-Guided Image Editing Shifts Emotion and Disengagement Timing in Social Media
- 通过扩散模型修改图像情绪特征，不依赖强制限制。
- 只有扩散方法能中和情绪且不降低画质，促进提前退出。
- 适合研究数字成瘾、人机交互与情感计算的学者。
互联网过度使用是当今数字社会的普遍现象。现有干预手段如时间限制或灰度化常依赖强制控制，易引发心理抗拒并被规避。基于已有研究——情绪反应介导内容消费与在线参与的关系，我们探究是否可通过调节图像情绪影响来非强制性地减少在线使用。本文系统分析三种回归引导的图像编辑方法：低级属性优化、潜在风格空间优化与基于扩散模型的编辑。前两者修改对比度、色彩等低级视觉特征，后者可实现服装、面部特征等高层语义调整。图像评分实验表明，仅扩散方法在不降低感知质量的前提下将情绪倾向调整至中性参考值。后续社交媒体实验显示，经编辑的图像使部分用户提前离线，显著缩短停留时间。
原文摘要 · Abstract (English)
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, spanning low-level attribute optimization, latent style-space optimization, and diffusion-based editing. While the first two modify low-level visual features (e.g., contrast, color), the diffusion-based method enables higher-level changes (e.g., adjusting clothing, facial features). A controlled image-rating study shows that only the diffusion-based approach shifts perceived emotion toward a neutral reference without reducing perceived quality. In a follow-up social media experiment, edited images were associated with earlier disengagement among users who left the feed.
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