Jiang, Y., Wang, Y., Song, X. et al. (1 more author) (2020) Comparing topic-aware neural networks for bias detection of news. In: De Giacomo, G., Catala, A., Dilkina, B., Milano, M., Barro, S., Bugarín, A. and Lang, J., (eds.) Proceedings of 24th European Conference on Artificial Intelligence (ECAI 2020). ECAI 2020 - 24th European Conference on Artificial Intelligence, 29 Aug - 02 Sep 2020, Santiago de Compostela, Spain. Frontiers in Artificial Intelligence and Applications, 325 . International Joint Conferences on Artificial Intelligence (IJCAI) , pp. 2054-2061. ISBN 9781643681009
Abstract
The commercial pressure on media has increasingly dominated the institutional rules of news media, and consequently, more and more sensational and dramatized frames and biases are in evidence in newspaper articles. Increased bias in the news media, which can result in misunderstanding and misuse of facts, leads to polarized opinions which can heavily influence the perspectives of the reader. This paper investigates learning models for detecting bias in the news. First, we look at incorporating into the models Latent Dirichlet Allocation (LDA) distributions which could enrich the feature space by adding word co-occurrence distribution and local topic probability in each document. In our proposed models, the LDA distributions are regarded as additive features on the sentence level and document level respectively. Second, we compare the performance of different popular neural network architectures incorporating these LDA distributions on a hyperpartisan newspaper article detection task. Preliminary experiment results show that the hierarchical models benefit more than non-hierarchical models when incorporating LDA features, and the former also outperform the latter.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Editors: |
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Copyright, Publisher and Additional Information: | © 2020 The authors and IOS Press. This article is published online with Open Access by IOS Press and distributed under the terms of the Creative Commons Attribution Non-Commercial License 4.0 (CC BY-NC 4.0). https://creativecommons.org/licenses/by-nc/4.0/deed.en_US |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 22 Apr 2020 06:51 |
Last Modified: | 14 Oct 2020 13:21 |
Status: | Published |
Publisher: | International Joint Conferences on Artificial Intelligence (IJCAI) |
Series Name: | Frontiers in Artificial Intelligence and Applications |
Refereed: | Yes |
Identification Number: | 10.3233/FAIA200327 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:159642 |