Podcasting has become a central infrastructure for the circulation and normalization of misogynistic and extremist ideologies, yet the scale, length, and affective density of audio content pose significant challenges for critical qualitative research. This paper introduces ManoWhisper, a feminist computational research infrastructure designed to support the large-scale analysis of misogynistic podcast ecosystems while preserving the contextual depth required for interpretive and ethical engagement. ManoWhisper combines automated audio acquisition, transcription, sentence-level classification, indexing, and visualization within a searchable web-based interface that enables researchers to move between computational pattern detection and close qualitative reading. Grounded in a feminist methodology of dwelling, the tool is designed to slow analysis, foreground emotional labour, and support collaborative research across varying levels of technical expertise. It allows for an in-depth consideration of more extremist media ecosystems across a variety of key factors. This paper documents ManoWhisper’s end-to-end pipeline, from content collection and transcription to classification, indexing, and interface design. Further we demonstrate its application across multiple peer-reviewed and public-facing research projects examining misogyny, masculinity, and gender-based extremism in podcasting, as well as how it is being used in policy and government institutions. We position ManoWhisper as methodological infrastructure that redistributes analytic capacity, makes repetition and scale visible, and enables ethically grounded engagement with harmful media. We conclude by reflecting on the tool’s limitations, its implications for feminist digital methods, and its relevance for understanding how misogynistic content circulates not only across media platforms but into emerging domains such as AI training data.
Ruest et al. (Fri,) studied this question.