Browsing by Author "Zoizner, Alon"

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    Can AI-Attributed News Challenge Partisan News Selection? Evidence from a Conjoint Experiment
    (SAGE Publications, 2025) Zoizner, Alon; Matthes, Jörg; Corbu, Nicoleta; De Vreese, Claes; Esser, Frank; Koc-Michalska, Karolina; Schemer, Christian; Theocharis, Yannis; Zilinsky, Jan
    With artificial intelligence (AI) increasingly shaping newsroom practices, scholars debate how citizens perceive news attributed to algorithms versus human journalists. Yet, little is known about these preferences in today’s polarized media environment, where partisan news consumption has surged. The current study explores this issue by providing a comprehensive and systematic examination of how citizens evaluate AI-attributed news compared to human-based news from like-minded and cross-cutting partisan sources. Using a preregistered conjoint experiment in the United States (N = 2,011) that mimics a high-choice media environment, we find that citizens evaluate AI-attributed news as negatively as cross-cutting news sources, both in terms of attitudes (perceived trustworthiness) and behavior (willingness to read the news story), while strongly preferring like-minded sources. These patterns remain stable across polarizing and non-polarizing issues and persist regardless of citizens’ preexisting attitudes toward AI, political extremity, and media trust. Our findings thus challenge more optimistic views about AI’s potential to facilitate exposure to diverse viewpoints. Moreover, they suggest that increased automation of news production faces both public mistrust and substantial reader resistance, raising concerns about the future viability of AI in journalism.
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    The Effects of the COVID-19 Outbreak on Selective Exposure: Evidence from 17 Countries
    (Taylor & Francis, 2022) Zoizner, Alon; Sheafer, Tamir; Castro, Laia; Aalberg, Toril; Cardenal, Ana S.; Corbu, Nicoleta
    A widely believed claim is that citizens tend to selectively expose themselves to like-minded information. However, when individuals find the information useful, they are more likely to consume cross-cutting sources. While crises such as terror attacks and pandemics can enhance the utility of cross-cutting information, empirical evidence on the role of real-world external threats in selective exposure is scarce. This paper examines the COVID-19 pandemic as a case study to test the extent to which citizens were exposed to information from cross-cutting sources on traditional and social media after the outbreak. Utilizing a two-wave panel survey among 14,218 participants across 17 countries – conducted before and after the initial outbreak – we show that citizens concerned about COVID-19 were more exposed to cross-cutting information on traditional and social media. The positive relationship with cross-cutting exposure to traditional news was stronger in countries where governments adopted less stringent policy responses, and in countries with greater pandemic severity and weaker democratic institutions. Our comparative approach thus sheds light on the social and political contexts in which cross-cutting exposure can occur.