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Title:Progress feedback for countering selective exposure : when visualization can backfire
Authors:ID Riemann, Janine (Author)
ID Alt, Jasmin (Author)
ID Sergaš, Uroš (Author)
ID Tkalčič, Marko (Author)
ID Ferwerda, Bruce (Author)
Files:.pdf RAZ_Riemann_Janine_2026.pdf (534,45 KB)
MD5: 5AD716DCD9799B03812A00E0EDADE1D8
 
URL https://dl.acm.org/doi/10.1145/3774935.3812732
 
Language:English
Work type:Unknown
Typology:1.08 - Published Scientific Conference Contribution
Organization:FAMNIT - Faculty of Mathematics, Science and Information Technologies
Abstract:Selective 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.
Keywords:human-centered computing, human computer interaction, information systems, recommender systems
Publication version:Author Accepted Manuscript
Year of publishing:2026
Number of pages:Str. 536-539
PID:20.500.12556/RUP-23117 This link opens in a new window
UDC:004.5
DOI:10.1145/3774935.3812732 This link opens in a new window
COBISS.SI-ID:280680195 This link opens in a new window
Publication date in RUP:05.06.2026
Views:29
Downloads:2
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Record is a part of a monograph

Title:UMAP 2026 : proceedings of the 34th ACM International Conference on User Modeling, Adaptation and Personalization
Place of publishing:New York (NY)
Publisher:Association for Computing Machinery
ISBN:979-8-4007-2311-7
COBISS.SI-ID:280614403 This link opens in a new window

Licences

License:CC BY 4.0, Creative Commons Attribution 4.0 International
Link:http://creativecommons.org/licenses/by/4.0/
Description:This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.

Secondary language

Language:Slovenian
Abstract:Selektivna izpostavljenost pri spletnih novicah se pogosto pripisuje mehanizmom personalizacije in uporabniškemu modeliranju. Nedavno delo predlaga posege na ravni vmesnika, ki vizualizirajo ravnotežje pri branju ali izpostavljenost navzkrižnim stališčem predstavijo kot napredek. Vendar nimamo dovolj empiričnih dokazov o tem, ali alternativne predstavitve povratnih informacij uporabniškega modela pomembno vplivajo na ukvarjanje z vsebinami, ki nasprotujejo prepričanjem uporabnikov. Izvedli smo eksperiment med skupinami (N = 84) v nadzorovanem novičarskem okolju, v katerem smo primerjali dve predstavitvi povratnih informacij o raznolikosti: (1) analitično vizualizacijo pristranskosti, ki povzema ravnotežje med vidiki, in (2) metaforično vizualizacijo rasti, ki izpostavljenost navzkrižnim stališčem uokvirja kot osebni razvoj. Glede na vedenjske in stališčne mere odprtomiselne vključenosti nobena od predstavitev povratnih informacij ni povečala vključenosti v primerjavi s kontrolno skupino, prav tako se obe zasnovi med seboj nista zanesljivo razlikovali. Rezultati kažejo, da lahke predstavitve signalov raznolikosti brez prilagodljive personalizacije ali strukturnih sprememb priporočil morda ne zadoščajo za spreminjanje selektivne izpostavljenosti v enosejnih okoljih. Razpravljamo o posledicah za oblikovanje povratnih informacij uporabniškega modela in depolarizacijskih ciljev v priporočilnih sistemih.
Keywords:računalništvo, usmerjeno v človeka, interakcija med človekom in računalnikom, informacijski sistemi, sistemi za priporočanje


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