research-article

Media Bias Characterization in Brazilian Presidential Elections

Authors:

Adriano VelosoAuthors Info & Claims

HT '19: Proceedings of the 30th ACM Conference on Hypertext and Social Media

Pages 231 - 240

https://doi.org/10.1145/3342220.3343656

Published: 12 September 2019 Publication History

Abstract

News media bias is commonly associated with framing information so as to influence readers judgments. One way to expose such bias is to compare different news outlets on the same stories and look for divergences. In this paper, we investigate news media bias in the context of Brazilian presidential elections by comparing four popular news outlets during three consecutive election years (2010, 2014, and 2018). We analyse the textual content of news stories in search for three kinds of bias: coverage, association, and subjective language. Coverage bias has to do with differences in mention rates of candidates and parties. Association bias occurs when, for example, one candidate is associated with a negative concept while another not. Subjective bias, in turn, has to do with wording that attempts to influence the readers by appealing to emotion, stereotypes, or persuasive language. We perform a thorough analysis on a large scale news data set where several of such biases are exposed.

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Cited By

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https://doi.org/10.1109/ACCESS.2023.3270630
Müller PChan CLudwig KFreudenthaler RWessler H(2023)Differential Racism in the News: Using Semi-Supervised Machine Learning to Distinguish Explicit and Implicit Stigmatization of Ethnic and Religious Groups in Journalistic DiscoursePolitical Communication10.1080/10584609.2023.219314640:4(396-414)Online publication date: 21-Mar-2023
https://doi.org/10.1080/10584609.2023.2193146
Akbar ACaton SBierig R(2023)Personalised Filter Bias with Google and DuckDuckGo: An Exploratory StudyArtificial Intelligence and Cognitive Science10.1007/978-3-031-26438-2_39(502-513)Online publication date: 23-Feb-2023
https://doi.org/10.1007/978-3-031-26438-2_39
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Index Terms

Media Bias Characterization in Brazilian Presidential Elections
1. Applied computing
  1. Computers in other domains
    1. Computing in government
      1. Voting / election technologies

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Published In

cover image ACM Conferences

HT '19: Proceedings of the 30th ACM Conference on Hypertext and Social Media

September 2019

326 pages

ISBN:9781450368858

DOI:10.1145/3342220

General Chairs:
Claus Atzenbeck
Hof University, Germany
,
Jessica Rubart
OWL University of Applied Sciences and Arts, Germany
,
Program Chair:
David E. Millard
University of Southampton, UK

Copyright © 2019 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]

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Published: 12 September 2019

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HT '19: 30th ACM Conference on Hypertext and Social Media

September 17 - 20, 2019

Hof, Germany

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HT '19 Paper Acceptance Rate 20 of 68 submissions, 29%;

Overall Acceptance Rate 378 of 1,158 submissions, 33%

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Cited By

Ness EFatima AOghaz M(2023)Data Driven Model to Investigate Political Bias in Mainstream MediaIEEE Access10.1109/ACCESS.2023.327063011(41880-41893)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3270630
Müller PChan CLudwig KFreudenthaler RWessler H(2023)Differential Racism in the News: Using Semi-Supervised Machine Learning to Distinguish Explicit and Implicit Stigmatization of Ethnic and Religious Groups in Journalistic DiscoursePolitical Communication10.1080/10584609.2023.219314640:4(396-414)Online publication date: 21-Mar-2023
https://doi.org/10.1080/10584609.2023.2193146
Akbar ACaton SBierig R(2023)Personalised Filter Bias with Google and DuckDuckGo: An Exploratory StudyArtificial Intelligence and Cognitive Science10.1007/978-3-031-26438-2_39(502-513)Online publication date: 23-Feb-2023
https://doi.org/10.1007/978-3-031-26438-2_39
Kamal SGullic JBagavathi A(2022)Modeling Polarization on Social Media Posts: A Heuristic Approach Using Media BiasFoundations of Intelligent Systems10.1007/978-3-031-16564-1_4(35-43)Online publication date: 26-Sep-2022
https://doi.org/10.1007/978-3-031-16564-1_4
Caled DSilva M(2022)A Transfer Learning Analysis of Political Leaning Classification in Cross-domain ContentComputational Processing of the Portuguese Language10.1007/978-3-030-98305-5_25(267-277)Online publication date: 16-Mar-2022
https://doi.org/10.1007/978-3-030-98305-5_25
Vieira LJeronimo CCampelo CMarinho Lde Salles Soares Neto C(2020)Analysis of the Subjectivity Level in Fake News FragmentsProceedings of the Brazilian Symposium on Multimedia and the Web10.1145/3428658.3430978(233-240)Online publication date: 30-Nov-2020
https://dl.acm.org/doi/10.1145/3428658.3430978

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