The news media shape public opinion, and often, the visual bias they contain is evident for careful human observers. This bias can be inferred from how different media sources portray different subjects or topics. In this paper, we model visual political bias in contemporary media sources at scale, using webly supervised data. We collect a dataset of over one million unique images and associated news articles from left- and right-leaning news sources, and develop a method to predict the image’s political leaning. This problem is particularly challenging because of the enormous intra-class visual and semantic diversity of our data. We propose two stages of training to tackle this problem. In the first stage, the model is forced to learn relevant visual concepts that, when joined with document embeddings computed from articles paired with the images, enable the model to predict bias. In the second stage, we remove the requirement of the text domain and train a visual classifier from the features of the former model. We show this two-stage approach that relies on an auxiliary task leveraging text, facilitates learning and outperforms several strong baselines. We present extensive quantitative and qualitative results analyzing our dataset. Our results reveal disparities in how different sides of the political spectrum portray individuals, groups, and topics.

新闻媒体塑造了公众舆论，而且它们所包含的视觉偏见通常对于细心的人类观察者来说是显而易见的。这种偏见可以从不同的媒体来源如何描绘不同的主题或话题中推断出来。在本文中，我们使用网络监督数据对当代媒体资源中的视觉政治偏见进行大规模建模。我们从左倾和右倾新闻来源收集了超过一百万张独特图像和相关新闻文章的数据集，并开发了一种方法来预测图像的政治倾向。由于我们的数据具有巨大的类内视觉和语义多样性，因此这个问题尤其具有挑战性。我们提出了两个阶段的训练来解决这个问题。在第一阶段，模型被迫学习相关的视觉概念，当与从与图像配对的文章计算的文档嵌入结合时，使模型能够预测偏差。在第二阶段，我们去除了文本域的要求，并从前一个模型的特征中训练了一个视觉分类器。我们展示了这种依赖于利用文本的辅助任务的两阶段方法，促进学习并优于几个强大的基线。我们提供了广泛的定量和定性结果分析我们的数据集。我们的结果揭示了政治光谱的不同方面如何描绘个人、群体和话题的差异。我们提供了广泛的定量和定性结果分析我们的数据集。我们的结果揭示了政治光谱的不同方面如何描绘个人、群体和话题的差异。我们提供了广泛的定量和定性结果分析我们的数据集。我们的结果揭示了政治光谱的不同方面如何描绘个人、群体和话题的差异。