Regressor-Guided Generative Image Editing Balances User Emotions to Reduce Time Spent Online

TL;DR

This paper explores emotion-guided image editing techniques to influence user emotions, aiming to reduce online time without restrictive controls, by balancing emotional responses through advanced image modification methods.

Contribution

It introduces three regressor-guided image editing approaches, including a novel diffusion-based method, to regulate emotional impact and decrease online engagement.

Findings

Diffusion-based edits effectively balance emotional responses.

Emotion regulation via image editing reduces online usage duration.

High visual quality is maintained in the edited images.

Abstract

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.