Regressor-Guided Image Editing Regulates Emotional Response to Reduce Online Engagement

2025-01-21

AI Summary

Excessive online engagement is driven by emotional contagion from images, necessitating interventions that reduce user interaction while preserving visual fidelity. Method: This paper proposes three emotion-regression-guided image editing techniques to modulate image valence—thereby dampening user engagement—while balancing affective efficacy and perceptual quality. We (i) integrate an emotion regression model into the entire image editing pipeline for the first time; (ii) leverage diffusion models for semantic-level, lossless emotional modulation, incorporating both classifier-guided and classifier-free guidance strategies; and (iii) develop two alternative approaches based on global geometric transformations and StyleGAN latent-space optimization. Results: Behavioral experiments show the diffusion-based methods significantly attenuate viewers’ positive emotional responses (p < 0.01) and achieve high subjective image quality (4.6/5.0), whereas the other methods only alter low-level visual features.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Humans and AI: Emotional Intelligence
• Cognitive Modeling & Cognitive Systems: Affective Computing

Application Category

• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Web Mining and Content Analysis: Content-based information diffusion

Abstract

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.

Problem

• Online Time Reduction
• Emotional Contagion
• Internet Overuse

Innovation

• Image Modification Techniques
• Emotional Response Alteration
• Online Behavior Regulation