论文标题
我们可以发现您的偏见：预测新闻文章的政治意识形态
We Can Detect Your Bias: Predicting the Political Ideology of News Articles
论文作者
论文摘要
我们探讨了预测新闻文章的主要政治意识形态或偏见的任务。首先，我们收集并发布了34,737篇文章的大型数据集，这些数据集是针对政治意识形态，左中心或右 - 的手动注释的，这些数据集在主题和媒体中都均衡。我们进一步使用一个充满挑战的实验设置，其中测试示例来自培训期间未见的媒体，这阻止了该模型学习检测目标新闻文章的来源，而不是预测其政治意识形态。从建模的角度来看，我们提出了一种对抗性媒体的适应以及特别适应的三胞胎损失。我们进一步添加了有关该来源的背景信息，我们表明这对于改善文章级预测非常有帮助。我们的实验结果表明，在这种具有挑战性的设置中，使用最先进的预训练的变压器的改进非常相当大。
We explore the task of predicting the leading political ideology or bias of news articles. First, we collect and release a large dataset of 34,737 articles that were manually annotated for political ideology -left, center, or right-, which is well-balanced across both topics and media. We further use a challenging experimental setup where the test examples come from media that were not seen during training, which prevents the model from learning to detect the source of the target news article instead of predicting its political ideology. From a modeling perspective, we propose an adversarial media adaptation, as well as a specially adapted triplet loss. We further add background information about the source, and we show that it is quite helpful for improving article-level prediction. Our experimental results show very sizable improvements over using state-of-the-art pre-trained Transformers in this challenging setup.
