Comparing Topic-Aware Neural Networks for Bias Detection of News

Ye Jiang, Yimin Wang, Xingyi Song, Diana Maynard

Abstract: The commercial pressure on media has increasingly dominated the institutional rules of news media, and consequently, more and more sensational and dramatized frames and biases are in evidence in newspaper articles. Increased bias in the news media, which can result in misunderstanding and misuse of facts, leads to polarized opinions which can heavily influence the perspectives of the reader. This paper investigates learning models for detecting bias in the news. First, we look at incorporating into the models Latent Dirichlet Allocation (LDA) distributions which could enrich the feature space by adding word co-occurrence distribution and local topic probability in each document. In our proposed models, the LDA distributions are regarded as additive features on the sentence level and document level respectively. Second, we compare the performance of different popular neural network architectures incorporating these LDA distributions on a hyperpartisan newspaper article detection task. Preliminary experiment results show that the hierarchical models benefit more than non-hierarchical models when incorporating LDA features, and the former also outperform the latter.