skip to main content
10.1145/3078714.3078724acmconferencesArticle/Chapter ViewAbstractPublication PageshtConference Proceedingsconference-collections
research-article

The Nature of Real and Perceived Bias in Chilean Media

Published:04 July 2017Publication History

ABSTRACT

News consumers expect news outlets to be objective and balanced in their reports of events. However, there is a body of evidence of bias in the media caused by underlying political and socio-economic viewpoints. Previous studies have tried to classify the partiality of the media, sometimes giving a quantitative evaluation, but there is little reported on its nature. The vast amount of content in the social media enables us to quantify the inclination of the press to either side of the political spectrum. To describe such tendencies, we use tweets to automatically compute a news outlet's political and socio-economic orientation. We show that the media have a measurable bias, and illustrate this by showing the favoritism of Chilean media for the ruling political parties in this country. We also found that the nature of the bias is reflected in the vocabulary used and the entities mentioned by different news outlets. A survey conducted among news consumers confirms that media bias has an impact on the coverage of controversial topics and that this is perceivable by the general audience. Having a more accurate method to measure and characterize media bias will clarify to the readers where outlets stand within the socio-economic landscape, even when a self-declared position is stated. This will empower readers to better reflect on the content provided by their news outlets of choice.

References

  1. Scott L. Althaus, Jill A. Edy, and Patricia F. Phalen. 2001. Using Substitutes for Full- Text News Stories in Content Analysis: Which Text Is Best? American Journal of Political Science 45, 3 (2001), 707--723. http://www.jstor.org/stable/2669247Google ScholarGoogle ScholarCross RefCross Ref
  2. Jisun An, Meeyoung Cha, Krishna Gummadi, and Jon Crowcroft. 2011. Media Landscape in Twitter: A World of New Conventions and Political Diversity. In Proceedings of the Fifth International Conference on Weblogs and Social Media. AAAI, Menlo Park, CA, USA.Google ScholarGoogle Scholar
  3. Johan Bollen, Alberto Pepe, and Huina Mao. 2009. Modeling public mood and emotion: Twitter sentiment and socio-economic phenomena. CoRR abs/0911.1583 (2009). http://arxiv.org/abs/0911.1583Google ScholarGoogle Scholar
  4. Maurice Bryson and William McDill. 1968. The political spectrum: A bi- dimensional approach. Rampart Journal of Individualist Thought 4, 2 (1968), 19--26.Google ScholarGoogle Scholar
  5. Raviv Cohen and Derek Ruths. 2013. Classifying Political Orientation on Twitter: It's Not Easy!. In ICWSM, Emre Kiciman, Nicole B. Ellison, Bernie Hogan, Paul Resnick, and Ian Soboroff (Eds.). The AAAI Press. http://dblp.uni-trier.de/db/ conf/icwsm/icwsm2013.html#CohenR13Google ScholarGoogle Scholar
  6. M Conover, B Gonçalves, J Ratkiewicz, A Flammini, and F Menczer. Predicting the Political Alignment of Twitter Users. In Proceedings of 3rd IEEE Conference on Social Computing (SocialCom).Google ScholarGoogle Scholar
  7. Alexander Dallmann, Florian Lemmerich, Daniel Zoller, and Andreas Hotho. 2015. Media Bias in German Online Newspapers. In Proceedings of the 26th ACM Conference on Hypertext & Social Media (HT '15). ACM, 133--137. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. Daniel Dor. 2003. On newspaper headlines as relevance optimizers. Journal of Pragmatics 35, 5 (2003), 695--721.Google ScholarGoogle ScholarCross RefCross Ref
  9. Jenny Rose Finkel, Trond Grenager, and Christopher Manning. 2005. Incor- porating Non-local Information into Information Extraction Systems by Gibbs Sampling. In Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics (ACL '05). 363--370. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. Kiran Garimella, Gianmarco De Francisci Morales, Aristides Gionis, and Michael Mathioudakis. 2015. Quantifying Controversy in Social Media. CoRR abs/1507.05224 (2015). http://arxiv.org/abs/1507.05224 Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. Venkata Rama Kiran Garimella and Ingmar Weber. 2014. Co-following on Twitter. In Proceedings of the 25th ACM Conference on Hypertext and Social Media (HT '14). 249--254. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. Matthew Gentzkow and Jesse M. Shapiro. 2010. What Drives Media Slant? Evidence From U.S. Daily Newspapers. Econometrica 78, 1 (2010), 35--71.Google ScholarGoogle ScholarCross RefCross Ref
  13. Pierre Geurts, Damien Ernst, and Louis Wehenkel. 2006. Extremely randomized trees. Machine Learning 63, 1 (2006), 3--42. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. Jennifer Golbeck and Derek Hansen. 2011. Computing Political Preference Among Twitter Followers. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI '11). ACM, 1105--1108. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. Y. Gu, T. Chen, Y. Sun, and B. Wang. 2016. Ideology Detection for Twitter Users with Heterogeneous Types of Links. ArXiv e-prints (Dec. 2016). arXiv:1612.08207Google ScholarGoogle Scholar
  16. E.S. Herman and N. Chomsky. 1988. Manufacturing consent. Pantheon Books. https://books.google.cl/books?id=Up5sAAAAIAAJGoogle ScholarGoogle Scholar
  17. Chunyu Kit and Xiaoyue Liu. 2008. Measuring mono-word termhood by rank difference via corpus comparison. Terminology. International Journal of Theoretical and Applied Issues in Specialized Communication 14, 2 (2008), 204--229.Google ScholarGoogle Scholar
  18. Haokai Lu, James Caverlee, and Wei Niu. 2015. 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. 213--222. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. Aibek Makazhanov and Davood Rafiei. 2013. Predicting Political Preference of Twitter Users. In Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM '13). ACM, 298--305. Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. Felix Ming, Fai Wong, Chee Wei Tan, Soumya Sen, and Mung Chiang. 2013. Quantifying Political Leaning from Tweets and Retweets. In Proceedings of the 7th International AAAI Conference on Web and Social Media. AAAI Press, Boston, Massachusetts, USA.Google ScholarGoogle Scholar
  21. Delia Mocanu, Andrea Baronchelli, Nicola Perra, Bruno Gonçalves, Qian Zhang, and Alessandro Vespignani. 2013. The Twitter of Babel: Mapping World Languages through Microblogging Platforms. PLoS ONE 8, 4 (04 2013), e61981.Google ScholarGoogle Scholar
  22. DF Nolan. 1971. Classifying and Analysing Politico-Economic Systems. The Individualist (Jan. 1971), 5--11.Google ScholarGoogle Scholar
  23. Souneil Park, Seungwoo Kang, Sangyoung Chung, and Junehwa Song. 2012. A Computational Framework for Media Bias Mitigation. ACM Trans. Interact. Intell. Syst. 2, 2, Article 8 (2012), 32 pages. Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. Diego Saez-Trumper, Carlos Castillo, and Mounia Lalmas. 2013. Social Media News Communities: Gatekeeping, Coverage, and Statement Bias. In Proceedings of the 22Nd ACM International Conference on Information & Knowledge Management (CIKM '13). ACM, 1679--1684. Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. Hans Stadthagen-Gonzalez, Constance Imbault, Miguel A. Pérez Sánchez, and Marc Brysbaert. 2016. Norms of valence and arousal for 14,031 Spanish words. Behavior Research Methods (2016), 1--13.Google ScholarGoogle Scholar
  26. Saatviga Sudhahar, Thomas Lansdall-Welfare, Ilias Flaounas, and Nello Cristian- ini. 2012. ElectionWatch: Detecting Patterns in News Coverage of US Elections. In Proceedings of the Demonstrations at the 13th Conference of the European Chapter of the Association for Computational Linguistics (EACL '12). 82--86. Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. The Advocates for Self Government. 1985. The World's Smallest Political Quiz. (1985). {Online http://www.theadvocates.org/quiz/quiz.php; 12-August-2016}.Google ScholarGoogle Scholar
  28. Giang Tran, Mohammad Alrifai, and Eelco Herder. 2015. Timeline Summarization from Relevant Headlines. Springer International Publishing, Cham, 245--256.Google ScholarGoogle Scholar
  29. Giang Binh Tran and Eelco Herder. 2015. Detecting Filter Bubbles in Ongoing News Stories. In UMAP Extended Proceedings.Google ScholarGoogle Scholar
  30. Justine Zhang Cristian Danescu-Niculescu-Mizil Jure Leskovec Vlad Niculae, Caroline Suen. 2015. QUOTUS: The Structure of Political Media Coverage as Revealed by Quoting Patterns. In Proceedings of WWW 2015. Google ScholarGoogle ScholarDigital LibraryDigital Library
  31. Daniel Xiaodan Zhou, Paul Resnick, and Qiaozhu Mei. 2011. Classifying the Political Leaning of News Articles and Users from User Votes. In Proceedings of the International AAAI Conference on Web and Social Media.Google ScholarGoogle Scholar

Index Terms

  1. The Nature of Real and Perceived Bias in Chilean Media

        Recommendations

        Comments

        Login options

        Check if you have access through your login credentials or your institution to get full access on this article.

        Sign in
        • Published in

          cover image ACM Conferences
          HT '17: Proceedings of the 28th ACM Conference on Hypertext and Social Media
          July 2017
          336 pages
          ISBN:9781450347082
          DOI:10.1145/3078714

          Copyright © 2017 ACM

          Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

          Publisher

          Association for Computing Machinery

          New York, NY, United States

          Publication History

          • Published: 4 July 2017

          Permissions

          Request permissions about this article.

          Request Permissions

          Check for updates

          Qualifiers

          • research-article

          Acceptance Rates

          HT '17 Paper Acceptance Rate19of69submissions,28%Overall Acceptance Rate378of1,158submissions,33%

          Upcoming Conference

          HT '24
          35th ACM Conference on Hypertext and Social Media
          September 10 - 13, 2024
          Poznan , Poland

        PDF Format

        View or Download as a PDF file.

        PDF

        eReader

        View online with eReader.

        eReader