The field of digital media analysis is moving towards a more nuanced understanding of narrative structures and their impact on public opinion. Researchers are developing innovative methods to analyze complex forms of data, such as podcasts, and uncover the relationships between topics and narrative frames. This has significant implications for the study of influence in digital media and the development of more effective communication strategies. Meanwhile, the design of AI-driven research tools is becoming increasingly important, particularly in the context of mental health support and qualitative research. There is a growing recognition of the need for culturally sensitive and personalized chatbots that can effectively support underrepresented populations. Additionally, the development of safe and transparent qualitative AI systems is crucial for advancing multidisciplinary and mixed-methods research. Noteworthy papers include: Listening Between the Lines: Decoding Podcast Narratives with Language Modeling, which presents a novel frame-labeling methodology for analyzing podcast narratives. Designing Mental-Health Chatbots for Indian Adolescents: Mixed-Methods Evidence, a Boundary-Object Lens, and a Design-Tensions Framework, which contributes to the development of culturally sensitive chatbot design. Not Everything That Counts Can Be Counted: A Case for Safe Qualitative AI, which argues for the development of dedicated qualitative AI systems built from the ground up for interpretive research.