Regressor-Guided Image Editing Regulates Emotional Response to Reduce Online Engagement
This addresses the problem of mitigating emotional manipulation in online content for social media platforms, though it is incremental as it builds on existing emotion regulation insights.
The paper tackled the problem of reducing emotional impact in images to decrease online engagement by proposing three regressor-guided editing approaches, with results showing that only the diffusion-based method successfully altered viewers' emotional responses while maintaining high image quality.
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.