We propose a novel supervised learning approach for political ideology
prediction (PIP) that is capable of predicting out-of-distribution inputs. This
problem is motivated by the fact that manual data-labeling is expensive, while
self-reported labels are often scarce and exhibit significant selection bias.
We propose a novel statistical model that decomposes the document embeddings
into a linear superposition of two vectors; a latent neutral \emph{context}
vector independent of ideology, and a latent \emph{position} vector aligned
with ideology. We train an end-to-end model that has intermediate contextual
and positional vectors as outputs. At deployment time, our model predicts
labels for input documents by exclusively leveraging the predicted positional
vectors. On two benchmark datasets we show that our model is capable of
outputting predictions even when trained with as little as 5\% biased data, and
is significantly more accurate than the state-of-the-art. Through
crowd-sourcing we validate the neutrality of contextual vectors, and show that
context filtering results in ideological concentration, allowing for prediction
on out-of-distribution examples.

本文提出了一种新的监督学习方法，用于政治意识形态预测，该方法可以预测超出分布输入；在此基础上，提出了一种新的统计模型，将文档嵌入分解为两个向量的线性叠加 - 一个独立于意识形态的潜在中性上下文向量和一个与意识形态对齐的潜在位置向量；在两个基准数据集上，证明了该模型即使在仅使用 5％的偏向数据进行训练时也能够输出预测，并且比现有技术显着更准确。

过滤上下文减轻政治意识形态预测中的稀缺和选择偏差

Filtering Context Mitigates Scarcity and Selection Bias in Political Ideology Prediction

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.

该研究通过构建多模数据集和深度学习算法，探讨了基于视频和音频进行新闻 YouTube 频道的政治意识形态预测问题，并在文本和元数据的基础上通过使用声音信号，将偏见检测能力提高了 6% 以上。