  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.
