Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Predicting the Leading Political Ideology of YouTube Channels Using
  Acoustic, Textual, and Metadata Information   We address the problem of predicting the leading political ideology, i.e.,
left-center-right bias, for YouTube channels of news media. Previous work on
the problem has focused exclusively on text and on analysis of the language
used, topics discussed, sentiment, and the like. In contrast, here we study
videos, which yields an interesting multimodal setup. Starting with gold
annotations about the leading political ideology of major world news media from
Media Bias/Fact Check, we searched on YouTube to find their corresponding
channels, and we downloaded a recent sample of videos from each channel. We
crawled more than 1,000 YouTube hours along with the corresponding subtitles
and metadata, thus producing a new multimodal dataset. We further developed a
multimodal deep-learning architecture for the task. Our analysis shows that the
use of acoustic signal helped to improve bias detection by more than 6%
absolute over using text and metadata only. We release the dataset to the
research community, hoping to help advance the field of multi-modal political
bias detection.

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The categories of this paper are: hep-ph
12
The categories of this paper are: nucl-th
16
The categories of this paper are: stat
36
The categories of this paper are: cmp-lg
14
The categories of this paper are: math-ph
15
The categories of this paper are: nucl-ex
20
The categories of this paper are: acc-phys
2
The categories of this paper are: math
21
The categories of this paper are: adap-org
35
The categories of this paper are: mtrl-th
18
The categories of this paper are: econ
9
The categories of this paper are: nlin
28
The categories of this paper are: alg-geom
37
The categories of this paper are: supr-con
6
The categories of this paper are: astro-ph
24
The categories of this paper are: dg-ga