@proceedings{evalu25,
title = {Evaluating Image Trust Labels in a News Recommender System},
author = {Svenja Lys Forstner and Yelyzaveta Lysova and Alain D. Starke and Christoph Trattner},
url = {https://mediafutures.no/ceur___inra_2025_short_workshop_paper-3/},
year = {2025},
date = {2025-09-26},
urldate = {2025-09-26},
issue = {INRA 2025/RecSys25},
abstract = {Rising user concerns about online misinformation and the spread of AI-generated visual content underscore the need for better ways to verify image authenticity. Image provenance labels are a proposed solution, aiming to help users assess the veracity of digital images. The Coalition for Content Provenance and Authenticity (C2PA), for instance, can disclose image provenance (i.e., origin or source details) to users in the form of labels that describe the image's metadata. However, little is known about whether users engage with or understand such labels, especially in news recommender contexts. In this paper, we introduce an alternative `Image Trust Score' label, inspired by the front-of-package Nutri-Score label, and experimentally evaluate its effectiveness in a personalized news setting. We present the results of a four-condition (no-label baseline, C2PA label, black-and-white and colored Image Trust Score) between-subjects study (N=202) in which participants selected news articles (with or without labels), reporting on label comprehension and trust. While image trust and article selection were not significantly affected, all labels increased article trust. The Image Trust Score was perceived as more understandable and appealing than the C2PA label, though many participants misinterpreted the labels' meaning. Our findings highlight the need for clearer and more intuitive provenance label design.},
keywords = {},
pubstate = {published},
tppubtype = {proceedings}
}
