Exploring Collective Action in Black Lives Matter Podcast Conversations

This thesis investigates how participation in collective action is articulated and evolves within podcast discussions, using the Black Lives Matter (BLM) movement as a case study. While research on collective action discourse has primarily focused on text-based content, this study takes a first step toward analyzing audio formats by using podcast transcripts. Using the Structured Podcast Research Corpus (SPoRC), we investigate how participation in collective action—categorized as problem-solution, call-to-action, intention, and execution—is expressed in spoken language. A multi-step filtering pipeline was developed to identify episodes discussing racial justice, followed by the extraction of participatory statements using a layered framework adapted from prior work on social media. We also examine the emotional dimensions of these statements, detecting ten key emotions and their association with varying stages of activism. The findings show that participation is often articulated through sequences of problem identification and solution proposal followed by calls to action and expressions of intention and execution. Emotional profiles vary by stage, with negative emotions dominating early stages, and positive emotions becoming more prominent during calls to action and execution. This contributes to a better understanding of how activism is expressed in spoken digital discourse and how emotional framing may influence the progression from awareness to action.