Abstract
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.- Anthology ID:
- D18-1388
- Volume:
- Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
- Month:
- October-November
- Year:
- 2018
- Address:
- Brussels, Belgium
- Venue:
- EMNLP
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 3518–3527
- Language:
- URL:
- https://aclanthology.org/D18-1388
- DOI:
- 10.18653/v1/D18-1388
- Cite (ACL):
- Vivek Kulkarni, Junting Ye, Steve Skiena, and William Yang Wang. 2018. Multi-view Models for Political Ideology Detection of News Articles. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 3518–3527, Brussels, Belgium. Association for Computational Linguistics.
- Cite (Informal):
- Multi-view Models for Political Ideology Detection of News Articles (Kulkarni et al., EMNLP 2018)
- PDF:
- https://preview.aclanthology.org/starsem-semeval-split/D18-1388.pdf