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.
@article{arxiv.1809.03485,
title = {Multi-view Models for Political Ideology Detection of News Articles},
author = {Vivek Kulkarni and Junting Ye and Steven Skiena and William Yang Wang},
journal= {arXiv preprint arXiv:1809.03485},
year = {2018}
}
Comments
10 pages. EMNLP 2018. Added copyright statement stating this is authors draft (also noticed and fixed issue with citation (spacing and readability))