Yoo Ji Suh

Despite overwhelming scientific consensus, climate change is a politicized issue in American politics (Chinn et al., 2020; Egan et al., 2022; McCright & Dunlap, 2011). Partisanship predicts environ...

We investigate how partisan media during and after the 2020 US presidential (Study 1) and 2022 midterm (Study 2) elections influenced preferences for unyielding politicians who do not compromise with partisan opponents. Our findings suggest that partisan media use may undermine willingness to support politicians who engage in deliberative compromises with opponents. This effect is likely driven by the tendency of partisan media to diminish willingness to engage in political listening and instigate moral considerations of the party’s policy goals.

This study examined the psychological mechanisms underlying the persuasive effects of moral reframing—matching message content to recipients’ core moral foundations—by focusing on processing fluency, message processing depth, and self-affirmation as mediators. An online experiment conducted in South Korea demonstrated that, across two policy issues, moral reframing promoted processing fluency and self-affirmation, thereby enhancing the perceived persuasiveness of policy advocacy messages. Furthermore, self-affirmation increased support for policies advocated by the messages. However, the depth of message processing did not differ between participants who read messages aligned with their moral foundations and those exposed to mismatched messages. In sum, the results suggest that moral reframing enhances persuasion by facilitating reflection on positive self-aspects and intuitive processing, rather than by encouraging deeper processing of the messages.

Generative Artificial Intelligence (AI) models like DALL·E 2 can interpret prompts and generate high-quality images that exhibit human creativity. Though public enthusiasm is booming, systematic auditing of potential gender biases in AI-generated images remains scarce. We addressed this gap by examining the prevalence of two occupational gender biases (representational and presentational biases) in 15,300 DALL·E 2 images spanning 153 occupations. We assessed potential bias amplification by benchmarking against the 2021 U.S. census data and Google Images. Our findings reveal that DALL·E 2 underrepresents women in male-dominated fields while overrepresenting them in female-dominated occupations. Additionally, DALL·E 2 images tend to depict more women than men with smiles and downward-pitching heads, particularly in female-dominated (versus male-dominated) occupations. Our algorithm auditing study demonstrates more pronounced representational and presentational biases in DALL·E 2 compared to Google Images and calls for feminist interventions to curtail the potential impacts of such biased AI-generated images on the media ecology.