On document representations for detection of biased news articles.

Detecting bias in text is an increasingly relevant topic, given the information overload problem. Automating this task is crucial for our needs of quality news consumption. With this in mind, we explore modern deep learning approaches, including contextualized word embeddings and attention mechanisms, to compare the effects of different document representation choices. We design token-wise, sentence-wise and hierarchical document representations. Focusing on hyperpartisan news detection, we show that hierarchical attention mechanisms are able to better capture information at different levels of granularity (including intra and inter-sentence), which seems to be relevant for this task. With an accuracy of 82.5%, our best performing system is based on an ensemble of hierarchical attention networks with ELMo embeddings, achieving state-of-the-art performance on the SemEval-2019 Task4 dataset.