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Title:Game on for news : stance-aware gamification in a news aggregator to promote engagement with diverse viewpoints
Authors:ID Frössling, Caroline (Author)
ID Sergaš, Uroš (Author)
ID Tkalčič, Marko (Author)
ID Ferwerda, Bruce (Author)
Files:.pdf RAZ_Frossling_Caroline_2026.pdf (1,05 MB)
MD5: 1EC19DC70B96AA098D411F2A6E2F9AAE
 
URL https://dl.acm.org/doi/10.1145/3774935.3812712
 
Language:English
Work type:Unknown
Typology:1.08 - Published Scientific Conference Contribution
Organization:FAMNIT - Faculty of Mathematics, Science and Information Technologies
Abstract:Personalized news systems often adapt ranking to user preferences, which can reinforce selective exposure. We investigate an alterna- tive personalization strategy: stance-aware incentive shaping. In- stead of modifying content ranking, we use a minimal per-topic stance user model to adapt reward gradients, awarding more points for engaging with counter-attitudinal content. In a between-subject experiment (� = 98), we compare a non- adaptive baseline with two adaptive incentive framings (levels vs. leaderboards). Stance-aware gamification increased behavioral en- gagement (clicks and time), while subjective engagement and com- prehension did not differ reliably. Only the level-based framing produced significant pre–post increases in stance change and open- minded thinking, with effects varying by topic. We position stance-aware incentive shaping as a lightweight user-model intervention that adapts motivational feedback rather than ranking, offering an alternative pathway for diversity-aware personalization in recommender systems.
Keywords:information systems, recommender systems, personalization, human-centered computing, user studies
Publication version:Author Accepted Manuscript
Year of publishing:2026
Number of pages:Str. 494-497
PID:20.500.12556/RUP-23115 This link opens in a new window
UDC:004.8:004.5
DOI:10.1145/3774935.3812712 This link opens in a new window
COBISS.SI-ID:280621059 This link opens in a new window
Publication date in RUP:05.06.2026
Views:31
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:Personalizirani novičarski sistemi pogosto prilagajajo razvrščanje vsebin uporabniškim preferencam, kar lahko krepi selektivno izpostavljenost. Preučujemo alternativno strategijo personalizacije: oblikovanje spodbud glede na stališče uporabnika. Namesto spreminjanja razvrstitve vsebin uporabimo minimalni uporabniški model stališča po posamezni temi, s katerim prilagajamo nagradne gradiente, tako da udeleženci prejmejo več točk za ukvarjanje z vsebinami, ki nasprotujejo njihovim stališčem. V eksperimentu med skupinami (N = 98) primerjamo neprilagojeno izhodiščno različico z dvema prilagojenima okvirjema spodbud: stopnjami in lestvicami najboljših. Gamifikacija, prilagojena stališču, je povečala vedenjsko vključenost, merjeno s kliki in časom, medtem ko se subjektivna vključenost in razumevanje nista zanesljivo razlikovala. Samo okvir s stopnjami je povzročil statistično značilno povečanje spremembe stališča in odprtomiselnega razmišljanja med merjenjem pred izpostavitvijo in po njej, pri čemer so se učinki razlikovali glede na temo. Oblikovanje spodbud glede na stališče umeščamo kot lahek poseg v uporabniški model, ki prilagaja motivacijsko povratno informacijo in ne razvrščanja vsebin, s čimer ponuja alternativno pot za personalizacijo, občutljivo na raznolikost, v priporočilnih sistemih.
Keywords:računalništvo, usmerjeno v človeka, interakcija med človekom in računalnikom, informacijski sistemi, sistemi za priporočanje


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