# Regressor-Guided Image Editing Shifts Emotion and Disengagement Timing in Social Media

arXiv: https://arxiv.org/abs/2501.12289 · HTML: https://mlsanity.com/paper/2501.12289/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 124 references that are in the map.
- First posted on arXiv 21 Jan 2025.

## Builds on (most cited first, 26 of 26)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020)
- [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021)
- [Emerging Properties in Self-Supervised Vision Transformers](https://mlsanity.com/paper/2104.14294/) (2021)
- [Scaling Rectified Flow Transformers for High-Resolution Image Synthesis](https://mlsanity.com/paper/2403.03206/) (2024)
- [InstructPix2Pix: Learning to Follow Image Editing Instructions](https://mlsanity.com/paper/2211.09800/) (2022)
- [Multimodal Unsupervised Image-to-Image Translation](https://mlsanity.com/paper/1804.04732/) (2018)
- [Guiding a Diffusion Model with a Bad Version of Itself](https://mlsanity.com/paper/2406.02507/) (2024)
- [Diverse Image-to-Image Translation via Disentangled Representations](https://mlsanity.com/paper/1808.00948/) (2018)
- [Kornia: an Open Source Differentiable Computer Vision Library for PyTorch](https://mlsanity.com/paper/1910.02190/) (2019)
- [Splicing ViT Features for Semantic Appearance Transfer](https://mlsanity.com/paper/2201.00424/) (2022)
- [GANalyze: Toward Visual Definitions of Cognitive Image Properties](https://mlsanity.com/paper/1906.10112/) (2019)
- [Eliminating Oversaturation and Artifacts of High Guidance Scales in Diffusion Models](https://mlsanity.com/paper/2410.02416/) (2024)
- [EditVal: Benchmarking Diffusion Based Text-Guided Image Editing Methods](https://mlsanity.com/paper/2310.02426/) (2023)
- [EmoEdit: Evoking Emotions through Image Manipulation](https://mlsanity.com/paper/2405.12661/) (2024)
- [EmotiCrafter: Text-to-Emotional-Image Generation based on Valence-Arousal Model](https://mlsanity.com/paper/2501.05710/) (2025)
- ['I Just Want to Hack Myself to Not Get Distracted': Evaluating Design Interventions for Self-Control on Facebook](https://mlsanity.com/paper/2001.04180/) (2020)
- [Building Emotional Machines: Recognizing Image Emotions through Deep Neural Networks](https://mlsanity.com/paper/1705.07543/) (2017)
- [Look here! A parametric learning based approach to redirect visual attention](https://mlsanity.com/paper/2008.05413/) (2020)
- [Image Sentiment Transfer](https://mlsanity.com/paper/2006.11337/) (2020)
- [Self-Control in Cyberspace: Applying Dual Systems Theory to a Review of Digital Self-Control Tools](https://mlsanity.com/paper/1902.00157/) (2019)
- [Automatic Image Transformation for Inducing Affect](https://mlsanity.com/paper/1707.08148/) (2017)
- [Global Image Sentiment Transfer](https://mlsanity.com/paper/2006.11989/) (2020)
- [InteractOut: Leveraging Interaction Proxies as Input Manipulation Strategies for Reducing Smartphone Overuse](https://mlsanity.com/paper/2401.16668/) (2024)
- [The Dynamics of Emotions in Online Interaction](https://mlsanity.com/paper/1605.03757/) (2016)

## Built on by (most cited first, 2 of 2)

- [What Can I Edit? Open-Ended Strategy Discovery and the Emotion Editability Landscape](https://mlsanity.com/paper/2607.23920/) (2026)
- [Learned Parametric Emotion Editing: Real-Time Affective Filtering for On-Device Social Media Video](https://mlsanity.com/paper/2609.21624/) (2026)

## Abstract (arXiv)

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.