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        <title>Multi-view Models for Political Ideology Detection of News Articles</title>
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        <namePart type="given">Vivek</namePart>
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    <name type="personal">
        <namePart type="given">Junting</namePart>
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            <title>Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing</title>
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            <namePart type="given">Ellen</namePart>
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            <namePart type="given">David</namePart>
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            <namePart type="given">Jun’ichi</namePart>
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    <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.</abstract>
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    <identifier type="doi">10.18653/v1/D18-1388</identifier>
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        <date>2018-oct-nov</date>
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%0 Conference Proceedings
%T Multi-view Models for Political Ideology Detection of News Articles
%A Kulkarni, Vivek
%A Ye, Junting
%A Skiena, Steve
%A Wang, William Yang
%Y Riloff, Ellen
%Y Chiang, David
%Y Hockenmaier, Julia
%Y Tsujii, Jun’ichi
%S Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
%D 2018
%8 oct nov
%I Association for Computational Linguistics
%C Brussels, Belgium
%F kulkarni-etal-2018-multi
%X 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.
%R 10.18653/v1/D18-1388
%U https://aclanthology.org/D18-1388
%U https://doi.org/10.18653/v1/D18-1388
%P 3518-3527

Markdown (Informal)

[Multi-view Models for Political Ideology Detection of News Articles](https://aclanthology.org/D18-1388) (Kulkarni et al., EMNLP 2018)

• Multi-view Models for Political Ideology Detection of News Articles (Kulkarni et al., EMNLP 2018)

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.