Everything We Hear: Towards Tackling Misinformation in Podcasts
Sachin Pathiyan Cherumanal, Ujwal Gadiraju, Damiano Spina
TL;DR
The paper articulates the problem of misinformation spread in podcasts and proposes a real-time auditory alert framework to notify listeners without interrupting the listening experience. It surveys auditory interventions, emphasizing non-speech auditory icons (including iconic/nomic and metaphorical mappings) and discusses optimal placement strategies to balance salience with listening flow. It also analyzes human factors, cognitive biases, and training requirements, outlining a research agenda for controlled experiments using synthesized podcast content and carefully designed stimuli. If successful, the approach could enhance listener awareness and critical appraisal of podcast content, offering a scalable modality for mitigating misinformation in audio media.
Abstract
Advances in generative AI, the proliferation of large multimodal models (LMMs), and democratized open access to these technologies have direct implications for the production and diffusion of misinformation. In this prequel, we address tackling misinformation in the unique and increasingly popular context of podcasts. The rise of podcasts as a popular medium for disseminating information across diverse topics necessitates a proactive strategy to combat the spread of misinformation. Inspired by the proven effectiveness of \textit{auditory alerts} in contexts like collision alerts for drivers and error pings in mobile phones, our work envisions the application of auditory alerts as an effective tool to tackle misinformation in podcasts. We propose the integration of suitable auditory alerts to notify listeners of potential misinformation within the podcasts they are listening to, in real-time and without hampering listening experiences. We identify several opportunities and challenges in this path and aim to provoke novel conversations around instruments, methods, and measures to tackle misinformation in podcasts.
