Traditional media outlets are known to report political news in a biased way,
potentially affecting the political beliefs of the audience and even altering
their voting behaviors. Many researchers focus on automatically detecting and
identifying media bias in the news, but only very few studies exist that
systematically analyze how theses biases can be best visualized and
communicated. We create three manually annotated datasets and test varying
visualization strategies. The results show no strong effects of becoming aware
of the bias of the treatment groups compared to the control group, although a
visualization of hand-annotated bias communicated bias instances more
effectively than a framing visualization. Showing participants an overview
page, which opposes different viewpoints on the same topic, does not yield
differences in respondents' bias perception. Using a multilevel model, we find
that perceived journalist bias is significantly related to perceived political
extremeness and impartiality of the article.

研究表明，媒体的政治偏见会影响受众的政治信仰及投票行为。通过三个手动注释的数据集及不同的可视化策略测试，结果表明手动注释偏见的可视化比框架可视化更有效。通过多层次模型，可以发现记者的偏见与文章的政治极端性和公正性有显著联系。

启迪新闻消费者理解偏见新闻报道：关于媒体偏见感知和可视化的研究

Enabling News Consumers to View and Understand Biased News Coverage: A  Study on the Perception and Visualization of Media Bias

Deep neural network (DNN) models have recently obtained state-of-the-art
prediction accuracy for the transcription factor binding (TFBS) site
classification task. However, it remains unclear how these approaches identify
meaningful DNA sequence signals and give insights as to why TFs bind to certain
locations. In this paper, we propose a toolkit called the Deep Motif Dashboard
(DeMo Dashboard) which provides a suite of visualization strategies to extract
motifs, or sequence patterns from deep neural network models for TFBS
classification. We demonstrate how to visualize and understand three important
DNN models: convolutional, recurrent, and convolutional-recurrent networks. Our
first visualization method is finding a test sequence's saliency map which uses
first-order derivatives to describe the importance of each nucleotide in making
the final prediction. Second, considering recurrent models make predictions in
a temporal manner (from one end of a TFBS sequence to the other), we introduce
temporal output scores, indicating the prediction score of a model over time
for a sequential input. Lastly, a class-specific visualization strategy finds
the optimal input sequence for a given TFBS positive class via stochastic
gradient optimization. Our experimental results indicate that a
convolutional-recurrent architecture performs the best among the three
architectures. The visualization techniques indicate that CNN-RNN makes
predictions by modeling both motifs as well as dependencies among them.

本文提出了一个名为 Deep Motif Dashboard 的工具套件，提供了一系列深度学习网络模型中 DNA 定序信号及转录因子结合位点分类的可视化策略。其中，卷积递归架构在实验结果中表现最佳，其可视化技术指出了 CNN-RNN 模型在建模模式以及其中相互依赖的模体方面的性能。