Conference Agenda (All times are shown in Eastern Daylight Time)

The potential for harm caused by deepfakes has been widely recognized, with concern over its psychological, social, and political consequences. This study investigates how individuals from different age groups perceive the harms that deepfakes could bring through the lens of the third-person perception (TPP). An online survey was conducted with 132 young adults and 152 seniors on perceptions of physical, emotional, financial, societal, and relational harm. The findings reveal significant TPP across all harm types, with seniors exhibiting a stronger TPP effect than young adults. Our findings extend TPP research into the examination of deepfake harms, as well as calling for tailored and age-sensitive media literacy interventions and risk communication strategies.

Public discourse around climate change remains polarized despite scientific consensus on anthropogenic climate change (ACC). This study examines how “believers” and “skeptics” of ACC differ in their YouTube comment discourse. We analyzed 44,989 comments from 30 videos using a large language model (LLM) as a qualitative annotator, identifying ten distinct topics. These annotations were combined with social network analysis to examine engagement patterns. A linear mixed-effects model showed that comments about government policy and natural cycles generated significantly lower interaction compared to misinformation, suggesting these topics are ideologically settled points within communities. These patterns reflect motivated reasoning, where users selectively engage with content that aligns with their identity and beliefs. Our findings highlight the utility of LLMs for large-scale qualitative analysis and highlight how climate discourse is shaped not only by content, but by underlying cognitive and ideological motivations.

As AI-generated content (AIGC) becomes increasingly prevalent across digital platforms, understanding its impact on users' trust and attitudes toward this new technology is crucial. Using an experimental design, we examined how the presence or absence of disclosing the use of AI in content generation influences user engagement with AI-generated images and videos on social media platforms. Our experiment recruited 64 individuals (N control group =31 , N experiment group =33) from various social groups, with experiment group being explicitly informed of the usage of AI in content generation and control group not informed. User’s behavioral variances are captured and measured using eye-tracking devices during the experiment session. We also conducted interviews with participants to further explore their experiences and attitudes. The findings highlight the nuanced role of disclosure of AIGC in shaping user trust and offer practical implications for the ethical presentation of AI-generated content.