Multi-view Models for Political Ideology Detection of News Articles
Open accessAbstract
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.
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In contrast to our multi-dimensional approach that examines the ideology of the issue being discussed instead of the author stance, much of the recent work in computational linguistics has been dedicated to the latter while collapsing ideology to one dimension.
“In contrast to our multi-dimensional approach that examines the ideology of the issue being discussed instead of the author stance, much of the recent work in computational linguistics has been dedicated to the latter (detection of ideological bias in news media) while collapsing ideology to one dimension”
From Political Ideology and Polarization: A Multi-dimensional Approach (2022) · cites this paper as Kulkarni et al., 2018
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Political Ideology and Polarization: A Multi-dimensional Approach
Sinno, B., Oviedo, B., Atwell, K., Alikhani, M., & Li, J. J. (2022) · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · doi:10.18653/v1/2022.naacl-main.17 · Cited by 12 · see citing works
“In contrast to studying the bias or the stance of the author of the text via linguistic framing (Kulkarni et al., 2018; Kiesel et al., 2019; Baly et al., 2019, 2020; Chen et al., 2020; Stefanov et al., 2020), we”
BackgroundMethodInfluential
- Authors
- Kulkarni, V., Ye, J., Skiena, S., & Wang, W. Y.
- Affiliations
- University of California, Santa Barbara (US)
- Stony Brook University (US)
- Venue
- Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
- Published
- 2018
- Pages
- 3518-3527
- OpenAlex
- W2890953671
- Cited by
- 71
- References
- 1
- Citations by year
- 2026: 2 · 2025: 4 · 2024: 9 · 2023: 11 · 2022: 9 · 2021: 12 · 2020: 11 · 2019: 13
- FWCI
- 3.67
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Conference on Empirical Methods in Natural Language Processing (EMNLP) (publishedVersion, cc-by)
- Funders
- National Science Foundation (1546113, 1355990)
- Defense Advanced Research Projects Agency
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
- Social Sciences › General Social Sciences › Computational and Text Analysis Methods
- Physical Sciences › Computer Science › Artificial Intelligence › Hate Speech and Cyberbullying Detection
- Keywords
- news articles, multi-view models, attention mechanism, link structure, network representation learning, bias detection
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