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Bias-aware news analysis using matrix-based news aggregation

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Abstract

Media bias describes differences in the content or presentation of news. It is an ubiquitous phenomenon in news coverage that can have severely negative effects on individuals and society. Identifying media bias is a challenging problem, for which current information systems offer little support. News aggregators are the most important class of systems to support users in coping with the large amount of news that is published nowadays. These systems focus on identifying and presenting important, common information in news articles, but do not reveal different perspectives on the same topic. Due to this analysis approach, current news aggregators cannot effectively reveal media bias. To address this problem, we present matrix-based news aggregation, a novel approach for news exploration that helps users gain a broad and diverse news understanding by presenting various perspectives on the same news topic. Additionally, we present NewsBird, an open-source news aggregator that implements matrix-based news aggregation for international news topics. The results of a user study showed that NewsBird more effectively broadens the user’s news understanding than the list-based visualization approach employed by established news aggregators, while achieving comparable effectiveness and efficiency for the two main use cases of news consumption: getting an overview of and finding details on current news topics.

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Notes

  1. https://www.microsoft.com/en-us/translator/translatorapi.aspx.

  2. https://en.wikipedia.org/wiki/Portal:Current_events/2014_November_7  https://en.wikipedia.org/wiki/Portal:Current_events/2015_June_7.

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Acknowledgements

This work has been supported by the Carl Zeiss Foundation. We also thank the Microsoft Corporation for allowing us to translate our datasets to English. Furthermore, we thank the participants of our studies. Finally, we thank the anonymous reviewers for their valuable comments that significantly helped to improve this article.

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Authors and Affiliations

  1. University of Konstanz, Constance, Germany

    Felix Hamborg, Norman Meuschke & Bela Gipp

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  1. Felix Hamborg
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  2. Norman Meuschke
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  3. Bela Gipp
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Correspondence to Felix Hamborg.

Additional information

Part of the research described in this article has been published in the proceedings of the ACM/IEEE-CS Joint Conference on Digital Libraries 2017 [26].

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Hamborg, F., Meuschke, N. & Gipp, B. Bias-aware news analysis using matrix-based news aggregation. Int J Digit Libr 21, 129–147 (2020). https://doi.org/10.1007/s00799-018-0239-9

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