Television Discourse Decoded: Comprehensive Multimodal Analytics at Scale

In this paper, we tackle the complex task of analyzing televised debates, with a focus on a prime time news debate show from India. Previous methods, which often relied solely on text, fall short in capturing the multimodal essence of these debates. To address this gap, we introduce a comprehensive...

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Bibliographic Details
Published in:arXiv.org 2024-08
Main Authors: Agarwal, Anmol, Priyadarshi, Pratyush, Sinha, Shiven, Gupta, Shrey, Jangra, Hitkul, Kumaraguru, Ponnurangam, Garimella, Kiran
Format: Article
Language:English
Subjects:
Algorithms
Automation
Bias
Computer vision
Data analysis
Data collection
Debates
Multimedia
Speech recognition
Television
Toolkits
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Summary:In this paper, we tackle the complex task of analyzing televised debates, with a focus on a prime time news debate show from India. Previous methods, which often relied solely on text, fall short in capturing the multimodal essence of these debates. To address this gap, we introduce a comprehensive automated toolkit that employs advanced computer vision and speech-to-text techniques for large-scale multimedia analysis. Utilizing state-of-the-art computer vision algorithms and speech-to-text methods, we transcribe, diarize, and analyze thousands of YouTube videos of a prime-time television debate show in India. These debates are a central part of Indian media but have been criticized for compromised journalistic integrity and excessive dramatization. Our toolkit provides concrete metrics to assess bias and incivility, capturing a comprehensive multimedia perspective that includes text, audio utterances, and video frames. Our findings reveal significant biases in topic selection and panelist representation, along with alarming levels of incivility. This work offers a scalable, automated approach for future research in multimedia analysis, with profound implications for the quality of public discourse and democratic debate. To catalyze further research in this area, we also release the code, dataset collected and supplemental pdf.
ISSN:2331-8422