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We address the problem of predicting the leading political ideology, i.e.,left-center-right bias, for YouTube channels of news media. Previous work onthe problem has focused exclusively on text and on analysis of the languageused, topics discussed, sentiment, and the like. In contrast, here we studyvideos, which yields an interesting multimodal setup. Starting with goldannotations about the leading political ideology of major world news media fromMedia Bias/Fact Check, we searched on YouTube to find their correspondingchannels, and we downloaded a recent sample of videos from each channel. Wecrawled more than 1,000 YouTube hours along with the corresponding subtitlesand metadata, thus producing a new multimodal dataset. We further developed amultimodal deep-learning architecture for the task. Our analysis shows that theuse of acoustic signal helped to improve bias detection by more than 6%absolute over using text and metadata only. We release the dataset to theresearch community, hoping to help advance the field of multi-modal politicalbias detection.

Predicting the Leading Political Ideology of YouTube Channels Using Acoustic, Textual, and Metadata Information