Podcasts as a Medium for Participation in Collective Action: A Case Study of Black Lives Matter

2025-09-16

AI Summary

Existing collective action research overemphasizes textual data while neglecting spoken, digital audio media as discursive vehicles. This study addresses that gap by examining the role of audio—specifically Black Lives Matter (BLM) movement podcasts—in shaping collective action discourse. Method: Leveraging the Structured Podcast Repository Corpus (SPoRC), we apply automated speech-to-text transcription at scale for the first time in collective action analysis. We develop a hierarchical annotation framework to identify four types of participatory utterances (problem-solution, call-to-action, intention, execution) and integrate an eight-dimensional emotion recognition model. Contribution/Results: Findings reveal stage-specific emotional contours across the movement lifecycle; notably, negative emotions correlate inversely with participation intensity—challenging dominant affective mobilization theories. The study demonstrates podcasts’ distinctive representational capacity as emergent digital discourse platforms and advances methodology through scalable audio-based analysis, offering novel empirical evidence and conceptual refinement for collective action scholarship.

Technology Category

• Natural Language Processing: Discourse, Pragmatics & Argument Mining
• Knowledge Representation and Reasoning: Action, Change, and Causality
• Cognitive Modeling & Cognitive Systems: Affective Computing

Application Category

• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Social Networks and Social Media: Analysis of web-mediated communications, communities, and crowds

Abstract

We study how participation in collective action is articulated in 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 investigated spoken language expressions of participation in collective action, categorized as problem-solution, call-to-action, intention, and execution. We identified podcast episodes discussing racial justice after important BLM-related events in May and June of 2020, and extracted participatory statements using a layered framework adapted from prior work on social media. We examined the emotional dimensions of these statements, detecting eight key emotions and their association with varying stages of activism. We found that emotional profiles vary by stage, with different positive emotions standing out during calls-to-action, intention, and execution. We detected negative associations between collective action and negative emotions, contrary to theoretical expectations. Our work contributes to a better understanding of how activism is expressed in spoken digital discourse and how emotional framing may depend on the format of the discussion.

Problem

• Analyzing podcast discussions of Black Lives Matter activism
• Investigating emotional dimensions in participatory collective action statements
• Examining how emotional framing varies across activism stages

Innovation

• Analyzing podcast transcripts for spoken activism expressions
• Using layered framework to extract participatory statements
• Examining emotional dimensions across activism stages