News articles similarity for automatic media bias detection in Polish news portals
Katarzyna Baraniak, Marcin Sydow
DOI: http://dx.doi.org/10.15439/2018F359
Citation: Proceedings of the 2018 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 15, pages 21–24 (2018)
Abstract. Digital media have enormous impact on the public opinion. In the ideal world the news in public media should be presented in a fair and impartial way. In practice the information presented in digital media is often biased and may distort the opinion on a given entity/event or concept. It is important to work on tools that could support the detection and analysis of the information bias. One of the first steps it to study the methods of automatic detection of the articles reporting on the same topic, event or entity to further use them in comparative analysis or building a test or training set.
References
- http://sgjp.pl/morfeusz/morfeusz-siat.html.
- D. Gomez-Zara, M. Boon, and L. Birnbaum. Who is the hero, the villain, and the victim?: Detection of roles in news articles using natural language techniques. In 23rd International Conference on Intelligent User Interfaces, pages 311–315. ACM, 2018.
- A. Huang. Similarity measures for text document clustering. In Proceedings of the sixth new zealand computer science research student conference (NZCSRSC2008), Christchurch, New Zealand, pages 49–56, 2008.
- K. Lazaridou and R. Krestel. Identifying political bias in news articles. Bulletin of the IEEE TCDL, 12, 2016.
- Q. V. Le and T. Mikolov. Distributed representations of sentences and documents. In ICML, volume 32 of JMLR Workshop and Conference Proceedings, pages 1188–1196. JMLR.org, 2014.
- H. Lu, J. Caverlee, and W. Niu. Biaswatch: A lightweight system for discovering and tracking topic-sensitive opinion bias in social media. In Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, pages 213–222. ACM, 2015.
- K. R. McKeown, R. Barzilay, D. Evans, V. Hatzivassiloglou, J. L. Klavans, A. Nenkova, C. Sable, B. Schiffman, and S. Sigelman. Tracking and summarizing news on a daily basis with columbia’s newsblaster. In Proceedings of the second international conference on Human Language Technology Research, pages 280–285. Morgan Kaufmann Publishers Inc., 2002.
- P. Neculoiu, M. Versteegh, and M. Rotaru. Learning text similarity with siamese recurrent networks. In Proceedings of the 1st Workshop on Representation Learning for NLP, pages 148–157, 2016.
- J. Piskorski, M. Sydow, and D. Weiss. Exploring linguistic features for web spam detection: a preliminary study. In AIRWeb ’08: Proceedings of the 4th international workshop on Adversarial information retrieval on the web, pages 25–28, New York, NY, USA, 2008. ACM.
- M. Recasens, C. Danescu-Niculescu-Mizil, and D. Jurafsky. Linguistic models for analyzing and detecting biased language. In Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), volume 1, pages 1650–1659, 2013.
- K. Sheshadri, C.-W. Hang, and M. Singh. The causal link between news framing and legislation. arXiv preprint https://arxiv.org/abs/1802.05768, 2018.
- N. Tintarev and J. Masthoff. Similarity for news recommender systems. In Proceedings of the AHâĂŹ06 Workshop on Recommender Systems and Intelligent User Interfaces. Citeseer, 2006.