Media bias detection requires comprehensive integration of information
derived from multiple news sources. Sentence-level political bias detection in
news is no exception, and has proven to be a challenging task that requires an
understanding of bias in consideration of the context. Inspired by the fact
that humans exhibit varying degrees of writing styles, resulting in a diverse
range of statements with different local and global contexts, previous work in
media bias detection has proposed augmentation techniques to exploit this fact.
Despite their success, we observe that these techniques introduce noise by
over-generalizing bias context boundaries, which hinders performance. To
alleviate this issue, we propose techniques to more carefully search for
context using a bias-sensitive, target-aware approach for data augmentation.
Comprehensive experiments on the well-known BASIL dataset show that when
combined with pre-trained models such as BERT, our augmentation techniques lead
to state-of-the-art results. Our approach outperforms previous methods
significantly, obtaining an F1-score of 58.15 over state-of-the-art bias
detection task.

媒体偏见检测需要综合多个新闻来源的信息，以句子级别的政治偏见检测为例，通过考虑上下文中的偏见而进行的任务被证明是一项具有挑战性的任务。我们提出了一种更加小心地搜索上下文的技术，采用偏见敏感和目标感知的数据增强方法，通过与 BERT 等预训练模型的结合，在著名的 BASIL 数据集上进行了综合实验，取得了 58.15 的 F1 得分，明显优于先前方法的最新偏见检测任务。

新闻中的目标感知背景政治偏见检测

Target-Aware Contextual Political Bias Detection in News

We introduce a classification scheme for detecting political bias in long
text content such as newspaper opinion articles. Obtaining long text data and
annotations at sufficient scale for training is difficult, but it is relatively
easy to extract political polarity from tweets through their authorship. We
train on tweets and perform inference on articles. Universal sentence encoders
and other existing methods that aim to address this domain-adaptation scenario
deliver inaccurate and inconsistent predictions on articles, which we show is
due to a difference in opinion concentration between tweets and articles. We
propose a two-step classification scheme that uses a neutral detector trained
on tweets to remove neutral sentences from articles in order to align opinion
concentration and therefore improve accuracy on that domain. Our implementation
is available for public use at this https URL

本文提出了一种基于推文训练的两步分类方案，用于检测长文本中的政治偏见。该方案包括通过推特数据训练中性检测器，用于去除文章中的中性句子以实现意见集中，并提高了文章的预测准确性。