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Diversity in News Recommendation (Dagstuhl Perspectives Workshop 19482)

Authors Abraham Bernstein, Claes de Vreese, Natali Helberger, Wolfgang Schulz, Katharina Zweig, Christian Baden, Michael A. Beam, Marc P. Hauer, Lucien Heitz, Pascal Jürgens, Christian Katzenbach, Benjamin Kille, Beate Klimkiewicz, Wiebke Loosen, Judith Moeller, Goran Radanovic, Guy Shani, Nava Tintarev, Suzanne Tolmeijer, Wouter van Atteveldt, Sanne Vrijenhoek, Theresa Zueger

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Author Details

Abraham Bernstein
  • Universität Zürich, CH
Claes de Vreese
  • University of Amsterdam, NL
Natali Helberger
  • University of Amsterdam, NL
Wolfgang Schulz
  • Universität Hamburg, DE
Katharina Zweig
  • Technische Universität Kaiserslautern, DE
Christian Baden
  • The Hebrew University of Jerusalem, IL
Michael A. Beam
  • Kent State University, US
Marc P. Hauer
  • Technische Universität Kaiserslautern, DE
Lucien Heitz
  • Universität Zürich, CH
Pascal Jürgens
  • Johannes Gutenberg-Universität Mainz, DE
Christian Katzenbach
  • Alexander von Humboldt Institute for Internet and Society, DE
Benjamin Kille
  • Technische Universität Berlin, DE
Beate Klimkiewicz
  • University Jagiellonski, PL
Wiebke Loosen
  • Leibniz Institute for Media Research, Hans-Bredow-Institut, DE
Judith Moeller
  • University of Amsterdam, NL
Goran Radanovic
  • Max Planck Institute for Software Systems, DE
Guy Shani
  • Ben Gurion University, IL
Nava Tintarev
  • Maastricht University, NL
Suzanne Tolmeijer
  • Universität Zürich, CH
Wouter van Atteveldt
  • Vrije Universiteit Amsterdam, NL
Sanne Vrijenhoek
  • University of Amsterdam, NL
Theresa Zueger
  • Alexander von Humboldt Institute for Internet and Society, DE

Funding

Abraham Bernstein, Lucien, Heitz, and Suzanne Tolmeijer would like to thank the Haslerstiftung for their generous support of their their reserach on news recommender systems.

Acknowledgements

The participants wish to express their gratitude to Schloss Dagstuhl for their strong support and hosting of this workshop. The editors want to thank all participants for their contributions to the Dagstuhl Perspectives Workshop Manifesto.

Cite As Get BibTex

Abraham Bernstein, Claes de Vreese, Natali Helberger, Wolfgang Schulz, Katharina Zweig, Christian Baden, Michael A. Beam, Marc P. Hauer, Lucien Heitz, Pascal Jürgens, Christian Katzenbach, Benjamin Kille, Beate Klimkiewicz, Wiebke Loosen, Judith Moeller, Goran Radanovic, Guy Shani, Nava Tintarev, Suzanne Tolmeijer, Wouter van Atteveldt, Sanne Vrijenhoek, and Theresa Zueger. Diversity in News Recommendation (Dagstuhl Perspectives Workshop 19482). In Dagstuhl Manifestos, Volume 9, Issue 1, pp. 43-61, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021) https://doi.org/10.4230/DagMan.9.1.43

Abstract

News diversity in the media has for a long time been a foundational and uncontested basis for ensuring that the communicative needs of individuals and society at large are met. Today, people increasingly rely on online content and recommender systems to consume information challenging the traditional concept of news diversity. In addition, the very concept of diversity, which differs between disciplines, will need to be re-evaluated requiring an interdisciplinary investigation, which requires a new level of mutual cooperation between computer scientists, social scientists, and legal scholars. Based on the outcome of a interdisciplinary workshop, we have the following recommendations, directed at researchers, funders, legislators, regulators, and the media industry:  
- Conduct interdisciplinary research on news recommenders and diversity. 
- Create a safe harbor for academic research with industry data. 
- Strengthen the role of public values in news recommenders. 
- Create a meaningful governance framework for news recommenders. 
- Fund a joint lab to spearhead the needed interdisciplinary research, boost practical innovation, develop reference solutions, and transfer insights into practice.

Subject Classification

ACM Subject Classification
  • Information systems → Web services
  • Information systems → Information retrieval diversity
  • Applied computing → Psychology
  • Human-centered computing → Empirical studies in HCI
  • Applied computing → Sociology
  • Information systems → Digital libraries and archives
  • Human-centered computing → HCI theory, concepts and models
  • Applied computing → Economics
Keywords
  • News
  • recommender systems
  • diversity

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