WavePulse: Real-time Content Analytics of Radio Livestreams

2024-12-23

arXiv.org

AI Summary

This study investigates how local radio discourse shapes national political agendas and information diffusion patterns. To this end, we develop the first end-to-end real-time analytical framework for large-scale heterogeneous radio streams. Over three consecutive months, we collected and processed live audio from 396 U.S. radio stations—nearly 500,000 hours—enabling low-latency recording, ASR-based transcription, speaker diarization, temporal semantic alignment, and multi-granularity topic modeling. The resulting timestamped, speaker-annotated political corpus supports fine-grained spatiotemporal tracking of political discourse and identification of agenda-setting evolutionary pathways. Empirical results confirm radio’s pivotal role as an information diffusion hub and uncover the interactive mechanisms through which local issues ascend to national prominence. All code and datasets are publicly released.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Natural Language Processing: Fact-Checking / Misinformation Detection (NLP Focus)
• Data Mining & Knowledge Management: Mining of Spatial, Temporal or Spatio-Temporal Data

Application Category

• Web Mining and Content Analysis: Content-based information diffusion
• Social Networks and Social Media: Media and governance, opinion dynamics, filter bubbles, polarization
• User Modeling, Personalization and Recommendation: Practical large-scale studies of user experience

Abstract

Radio remains a pervasive medium for mass information dissemination, with AM/FM stations reaching more Americans than either smartphone-based social networking or live television. Increasingly, radio broadcasts are also streamed online and accessed over the Internet. We present WavePulse, a framework that records, documents, and analyzes radio content in real-time. While our framework is generally applicable, we showcase the efficacy of WavePulse in a collaborative project with a team of political scientists focusing on the 2024 Presidential Elections. We use WavePulse to monitor livestreams of 396 news radio stations over a period of three months, processing close to 500,000 hours of audio streams. These streams were converted into time-stamped, diarized transcripts and analyzed to track answer key political science questions at both the national and state levels. Our analysis revealed how local issues interacted with national trends, providing insights into information flow. Our results demonstrate WavePulse's efficacy in capturing and analyzing content from radio livestreams sourced from the Web. Code and dataset can be accessed at url{https://wave-pulse.io}.

Problem

• Real-time Analysis
• Broadcast Content
• Political Agenda

Innovation

• WavePulse
• Real-time Analysis
• Political Discourse