Progress feedback for countering selective exposureRiemann, Janine (Avtor) Alt, Jasmin (Avtor) Sergaš, Uroš (Avtor) Tkalčič, Marko (Avtor) Ferwerda, Bruce (Avtor) human-centered computinghuman computer interactioninformation systemsrecommender systemsSelective exposure in online news is often attributed to person- alization mechanisms and user modeling. Recent work proposes interface-level interventions that visualize reading balance or frame cross-cutting exposure as progress. However, we lack empirical evidence on whether alternative representations of user-model feedback meaningfully influence engagement with belief-opposing content. We conducted a between-subject experiment (N = 84) in a controlled news environment comparing two representations of diversity feedback: (1) an analytic bias visualization summarizing viewpoint balance and (2) a metaphorical growth visualization fram- ing cross-cutting exposure as personal development. Across behav- ioral and attitudinal measures of open-minded engagement, neither feedback representation increased engagement relative to control, and the two designs did not differ reliably. Our results suggest that lightweight representations of diversity signals—without adaptive personalization or structural changes to recommendations—may be insufficient to alter selective exposure in single-session settings. We discuss implications for designing user-model feedback and depolarization objectives in recommender systems.20262026-06-05 14:43:57Neznano23117sl