Game on for newsFrössling, Caroline (Avtor) Sergaš, Uroš (Avtor) Tkalčič, Marko (Avtor) Ferwerda, Bruce (Avtor) information systemsrecommender systemspersonalizationhuman-centered computinguser studiesPersonalized 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.20262026-06-05 11:43:55Neznano23115UDK: 004.8:004.5OceCobissID: 280614403DOI: 10.1145/3774935.3812712COBISS.SI-ID: 280621059sl