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Newsalyze: Effective Communication of Person-Targeting Biases in News Articles


Hamborg, Felix; Heinser, Kim; Zhukova, Anastasia; Donnay, Karsten; Gipp, Bela (2021). Newsalyze: Effective Communication of Person-Targeting Biases in News Articles. In: 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL), Champaign, IL, USA, 27 September 2021 - 30 September 2021. ACM/IEEE, 130-139.

Abstract

Media bias and its extreme form, fake news, can decisively affect public opinion. Especially when reporting on policy issues, slanted news coverage may strongly influence societal decisions, e.g., in democratic elections. Our paper makes three contributions to address this issue. First, we present a system for bias identification, which combines state-of-the-art methods from natural language understanding. Second, we devise bias-sensitive visualizations to communicate bias in news articles to non-expert news consumers. Third, our main contribution is a large-scale user study that measures bias-awareness in a setting that approximates daily news consumption, e.g., we present respondents with a news overview and individual articles. We not only measure the visualizations' effect on respondents' bias-awareness, but we can also pinpoint the effects on individual components of the visualizations by employing a conjoint design. Our bias-sensitive overviews strongly and significantly increase bias-awareness in respondents. Our study further suggests that our content-driven identification method detects groups of similarly slanted news articles due to substantial biases present in individual news articles. In contrast, the reviewed prior work rather only facilitates the visibility of biases, e.g., by distinguishing left- and right-wing outlets.

Abstract

Media bias and its extreme form, fake news, can decisively affect public opinion. Especially when reporting on policy issues, slanted news coverage may strongly influence societal decisions, e.g., in democratic elections. Our paper makes three contributions to address this issue. First, we present a system for bias identification, which combines state-of-the-art methods from natural language understanding. Second, we devise bias-sensitive visualizations to communicate bias in news articles to non-expert news consumers. Third, our main contribution is a large-scale user study that measures bias-awareness in a setting that approximates daily news consumption, e.g., we present respondents with a news overview and individual articles. We not only measure the visualizations' effect on respondents' bias-awareness, but we can also pinpoint the effects on individual components of the visualizations by employing a conjoint design. Our bias-sensitive overviews strongly and significantly increase bias-awareness in respondents. Our study further suggests that our content-driven identification method detects groups of similarly slanted news articles due to substantial biases present in individual news articles. In contrast, the reviewed prior work rather only facilitates the visibility of biases, e.g., by distinguishing left- and right-wing outlets.

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Additional indexing

Item Type:Conference or Workshop Item (Paper), refereed, original work
Communities & Collections:06 Faculty of Arts > Institute of Political Science
08 Research Priority Programs > Digital Society Initiative
Dewey Decimal Classification:320 Political science
Language:English
Event End Date:30 September 2021
Deposited On:26 Jan 2022 10:21
Last Modified:20 Mar 2022 07:50
Publisher:ACM/IEEE
OA Status:Green
Free access at:Publisher DOI. An embargo period may apply.
Publisher DOI:https://doi.org/10.1109/jcdl52503.2021.00025
Project Information:
  • : FunderHeidelberg Academy of Sciences and Humanities
  • : Grant ID
  • : Project Title
  • : FunderMinistry of Science, Research and the Arts of the State of Baden-Wurttemberg
  • : Grant ID
  • : Project Title
  • Content: Published Version
  • Language: English