It is known that media outlets, such as CNN and FOX, have intrinsic political bias that is reflected in their news reports. The computational prediction of such bias has broad application prospects. However, the prediction is difficult via directly analyzing the news content without high-level context. In contrast, social signals (e.g., the network structure of media followers) provide inspiring cues to uncover such bias. In this article, we realize the first attempt of predicting the latent bias of media outlets by analyzing their social network structures. In particular, we address two key challenges: network sparsity and label sparsity . The network sparsity refers to the partial sampling of the entire follower network in practical analysis and computing, whereas the label sparsity refers to the difficulty of annotating sufficient labels to train the prediction model. To cope with the network sparsity, we propose a hybrid sampling strategy to construct a training corpus that contains network information from micro to macro views. Based on this training corpus, a semi-supervised network embedding approach is proposed to learn low-dimensional yet effective network representations. To deal with the label sparsity, we adopt a graph-based label propagation scheme to supplement the missing links and augment label information for model training. The preceding two steps are iteratively optimized to reinforce each other. We further collect a large-scale dataset containing social networks of 10 media outlets together with about 300,000 followers and more than 5 million connections. Over this dataset, we compare our model to a range of state of the art. Superior performance gains demonstrate the merits of the proposed approach. More importantly, the experimental results and analyses confirm the validity of our approach for the computerized prediction of media bias.

中文翻译：

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通过社交网络学习发现媒体偏见

众所周知，CNN 和 FOX 等媒体具有内在的政治偏见，这反映在他们的新闻报道中。这种偏差的计算预测具有广阔的应用前景。然而，在没有高级上下文的情况下，通过直接分析新闻内容来进行预测是很困难的。相比之下，社会信号（例如，媒体追随者的网络结构）提供了启发性的线索来揭示这种偏见。在本文中，我们首次尝试通过分析媒体的社交网络结构来预测媒体的潜在偏见。特别是，我们解决了两个关键挑战：网络稀疏和标签稀疏性. 网络稀疏度是指在实际分析和计算中对整个follower网络进行部分采样，而标签稀疏度是指标注足够的标签来训练预测模型的难度。为了应对网络稀疏性，我们提出了一种混合采样策略来构建一个包含从微观到宏观视图的网络信息的训练语料库。基于这个训练语料库，提出了一种半监督的网络嵌入方法来学习低维但有效的网络表示。为了处理标签稀疏性，我们采用基于图的标签传播方案来补充缺失的链接并增加模型训练的标签信息。前两步经过迭代优化，相互加强。我们进一步收集了一个包含 10 家媒体的社交网络以及大约 300,000 名关注者和超过 500 万个连接的大规模数据集。在这个数据集上，我们将我们的模型与一系列最先进的模型进行比较。卓越的性能增益证明了所提出方法的优点。更重要的是，实验结果和分析证实了我们的媒体偏见计算机预测方法的有效性。