Multi-view Models for Political Ideology Detection of News Articles
This work addresses the need for more accurate political ideology detection in news articles, which is important for media analysis and bias monitoring, though it is incremental as it builds on existing neural and network methods.
The paper tackled the problem of detecting political ideology in news articles by proposing a multi-view model that uses textual content and network structure, achieving a 10 percentage point improvement in F1 score over state-of-the-art methods.
A news article's title, content and link structure often reveal its political ideology. However, most existing works on automatic political ideology detection only leverage textual cues. Drawing inspiration from recent advances in neural inference, we propose a novel attention based multi-view model to leverage cues from all of the above views to identify the ideology evinced by a news article. Our model draws on advances in representation learning in natural language processing and network science to capture cues from both textual content and the network structure of news articles. We empirically evaluate our model against a battery of baselines and show that our model outperforms state of the art by 10 percentage points F1 score.