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Newsalyze: enabling news consumers to understand media bias


Hamborg, Felix; Zhukova, Anastasia; Donnay, Karsten; Gipp, Bela (2020). Newsalyze: enabling news consumers to understand media bias. In: Proceedings of the ACM/IEEE Joint Conference on Digital Libraries, Virtual Event China, 1 August 2020 - 5 August 2020. ACM/IEEE, 455-456.

Abstract

News is a central source of information for individuals to inform themselves on current topics. Knowing a news article's slant and authenticity is of crucial importance in times of "fake news," news bots, and centralization of media ownership. We introduce Newsalyze, a bias-aware news reader focusing on a subtle, yet powerful form of media bias, named bias by word choice and labeling (WCL). WCL bias can alter the assessment of entities reported in the news, e.g., "freedom fighters" vs. "terrorists." At the core of the analysis is a neural model that uses a news-adapted BERT language model to determine target-dependent sentiment, a high-level effect of WCL bias. While the analysis currently focuses on only this form of bias, the visualizations already reveal patterns of bias when contrasting articles (overview) and in-text instances of bias (article view).

Abstract

News is a central source of information for individuals to inform themselves on current topics. Knowing a news article's slant and authenticity is of crucial importance in times of "fake news," news bots, and centralization of media ownership. We introduce Newsalyze, a bias-aware news reader focusing on a subtle, yet powerful form of media bias, named bias by word choice and labeling (WCL). WCL bias can alter the assessment of entities reported in the news, e.g., "freedom fighters" vs. "terrorists." At the core of the analysis is a neural model that uses a news-adapted BERT language model to determine target-dependent sentiment, a high-level effect of WCL bias. While the analysis currently focuses on only this form of bias, the visualizations already reveal patterns of bias when contrasting articles (overview) and in-text instances of bias (article view).

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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
Scopus Subject Areas:Physical Sciences > General Engineering
Language:English
Event End Date:5 August 2020
Deposited On:06 Jan 2021 15:43
Last Modified:20 Jun 2022 07:14
Publisher:ACM/IEEE
ISBN:978-1-4503-7585-6
Additional Information:JCDL '20: The ACM/IEEE Joint Conference on Digital Libraries in 2020 Autor der Publikation: Huang, Ruhua Publisher: Association for Computing Machinery, New York, NY
OA Status:Green
Publisher DOI:https://doi.org/10.1145/3383583.3398561
Related URLs:https://dl.acm.org/doi/10.1145/3383583.3398561 (Organisation)
  • Content: Published Version
  • Language: English
  • Licence: Creative Commons: Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)