More Web Proxy on the site http://driver.im/

short-paper

Open access

Deliberative Diversity for News Recommendations: Operationalization and Experimental User Study

Authors:

Juliane A. Lischka,

Laura Laugwitz,

Abraham BernsteinAuthors Info & Claims

RecSys '23: Proceedings of the 17th ACM Conference on Recommender Systems

Pages 813 - 819

https://doi.org/10.1145/3604915.3608834

Published: 14 September 2023 Publication History

All formats PDF

Abstract

News recommender systems are an increasingly popular field of study that attracts a growing interdisciplinary research community. As these systems play an essential role in our daily lives, the mechanisms behind their curation processes are under scrutiny. In the area of personalized news, many platforms make design choices driven by economic incentives. In contrast to such systems that optimize for financial gain, there can be norm-driven diversity systems that prioritize normative and democratic goals. However, their impact on users in terms of inducing behavioral change or influencing knowledge is still understudied. In this paper, we contribute to the field of news recommender system design by conducting a user study that examines the impact of these normative approaches. We a.) operationalize the notion of a deliberative public sphere for news recommendations, show b.) the impact on news usage, and c.) the influence on political knowledge, attitudes and voting behavior. We find that exposure to small parties is associated with an increase in knowledge about their candidates and that intensive news consumption about a party can change the direction of attitudes of readers towards the issues of the party.

References

[1]

Abraham Bernstein, Claes De Vreese, Natali Helberger, Wolfgang Schulz, Katharina Zweig, Lucien Heitz, Suzanne Tolmeijer, 2021. Diversity in News Recommendation. Dagstuhl Manifestos 9, 1 (2021), 43–61.

[2]

David Broockman and Joshua Kalla. 2022. The manifold effects of partisan media on viewers’ beliefs and attitudes: A field experiment with Fox News viewers. https://doi.org/10.31219/osf.io/jrw26

[3]

Laia Castro. 2021. Measuring Partisan Media Bias Cross–Nationally. 27, 2 (2021), 412–433. https://doi.org/10.1111/spsr.12459

[4]

Saul Cunow, Scott Desposato, Andrew Janusz, and Cameron Sells. 2021. Less is more: The paradox of choice in voting behavior. 69 (2021), 102230. https://doi.org/10.1016/j.electstud.2020.102230 PII: S0261379420301104.

[5]

Jakob-Moritz Eberl, Hajo G Boomgaarden, and Markus Wagner. 2017. One bias fits all? Three types of media bias and their effects on party preferences. Communication Research 44, 8 (2017), 1125–1148.

[6]

Myra Marx Ferree, William A Gamson, Jürgen Gerhards, and Dieter Rucht. 2002. Four models of the public sphere in modern democracies. Theory and society 31, 3 (2002), 289–324.

[7]

R. Kelly Garrett and Natalie Jomini Stroud. 2014. Partisan paths to exposure diversity. 64, 4 (2014), 680–701. https://doi.org/10.1111/jcom.12105

[8]

Sabine Geers and Linda Bos. 2017. Priming Issues, Party Visibility, and Party Evaluations: The Impact on Vote Switching. 34, 3 (2017), 344–366. https://doi.org/10.1080/10584609.2016.1201179

[9]

Jeremy Grosman and Tyler Reigeluth. 2019. Perspectives on algorithmic normativities: engineers, objects, activities. Big Data & Society 6, 2 (2019), 2053951719858742.

[10]

Jaron Harambam, Dimitrios Bountouridis, Mykola Makhortykh, and Joris van Hoboken. 2019. Designing for the Better by Taking Users into Account: A Qualitative Evaluation of User Control Mechanisms in (News) Recommender Systems. In Proceedings of the 13th ACM Conference on Recommender Systems (Copenhagen, Denmark) (RecSys ’19). Association for Computing Machinery, New York, NY, USA, 69–77. https://doi.org/10.1145/3298689.3347014

Digital Library

[11]

Lucien Heitz, Juliane A Lischka, Alena Birrer, Bibek Paudel, Suzanne Tolmeijer, Laura Laugwitz, and Abraham Bernstein. 2022. Benefits of diverse news recommendations for democracy: A user study. Digital Journalism 10, 10 (2022), 1710–1730.

[12]

Natali Helberger. 2019. On the democratic role of news recommenders. Digital Journalism 7, 8 (2019), 993–1012.

[13]

Natali Helberger, Max van Drunen, Judith Moeller, Sanne Vrijenhoek, and Sarah Eskens. 2022. Towards a Normative Perspective on Journalistic AI: Embracing the Messy Reality of Normative Ideals., 1605–1626 pages.

[14]

David Nicolas Hopmann, Rens Vliegenthart, Claes H. de Vreese, and Erik Albæk. 2010. Effects of Election News Coverage: How Visibility and Tone Influence Party Choice. 27, 4 (2010), 389–405. https://doi.org/10.1080/10584609.2010.516798

[15]

Mozhgan Karimi, Dietmar Jannach, and Michael Jugovac. 2018. News recommender systems–Survey and roads ahead. Information Processing & Management 54, 6 (2018), 1203–1227.

[16]

Bumsoo Kim, Ryan Broussard, and Matthew Barnidge. 2020. Testing political knowledge as a mediator of the relationship between news use and affective polarization. (2020), 1–13. https://doi.org/10.1080/03623319.2020.1750845

[17]

Silvia Knobloch-Westerwick. 2015. Choice and preference in media use. Routledge.

[18]

Katharina Ludwig, Alexander Grote, Andreea Iana, Mehwish Alam, Heiko Paulheim, Harald Sack, Christof Weinhardt, and Philipp Müller. 2023. Divided by the Algorithm? The (Limited) Effects of Content- and Sentiment-Based News Recommendation on Affective, Ideological, and Perceived Polarization. (2023). https://doi.org/10.1177/08944393221149290

Digital Library

[19]

Maxwell E McCombs and Donald L Shaw. 1972. The agenda-setting function of mass media. Public opinion quarterly 36, 2 (1972), 176–187.

[20]

Joanne M Miller and Jon A Krosnick. 2000. News media impact on the ingredients of presidential evaluations: Politically knowledgeable citizens are guided by a trusted source. American Journal of Political Science (2000), 301–315.

[21]

Eliza Mitova, Sina Blassnig, Edina Strikovic, Aleksandra Urman, Aniko Hannak, Claes H. de Vreese, and Frank Esser. 2022. News recommender systems: a programmatic research review. (2022), 1–30. https://doi.org/10.1080/23808985.2022.2142149

[22]

Diana C Mutz. 2006. Hearing the other side: Deliberative versus participatory democracy. Cambridge University Press.

[23]

Tim Neumann, Ole Kelm, and Marco Dohle. 2021. Polarisation and silencing others during the covid-19 pandemic in Germany: An experimental study using algorithmically curated online environments. Javnost-The Public 28, 3 (2021), 323–339.

[24]

Michael Quinn Patton. 2009. Qualitative research & evaluation methods (3. ed., [nachdr.] ed.). Sage.

[25]

Luca Rossetto, Matthias Baumgartner, Ralph Gasser, Lucien Heitz, Ruijie Wang, and Abraham Bernstein. 2021. Exploring Graph-querying approaches in LifeGraph. In Proceedings of the 4th Annual on Lifelog Search Challenge. 7–10.

Digital Library

[26]

Holli Sargeant, Eliska Pirkova, Matthias C Kettemann, Marlena Wisniak, Martin Scheinin, Emmi Bevensee, Katie Pentney, Lorna Woods, Lucien Heitz, Bojana Kostic, 2022. Spotlight on Artificial Intelligence and Freedom of Expression: A Policy Manual. Organization for Security and Co-operation in Europe (2022).

[27]

Rüdiger Schmitt-Beck, Sigrid Roßteutscher, Harald Schoen, Bernhard Weßels, and Christof Wolf. 2022. A new era of electoral instability. In The Changing German Voter. Oxford: Oxford University Press, 3–24.

[28]

Morten Skovsgaard and Kim Andersen. 2020. Conceptualizing news avoidance: Towards a shared understanding of different causes and potential solutions. Journalism studies 21, 4 (2020), 459–476.

[29]

Celina Treuillier, Sylvain Castagnos, Evan Dufraisse, and Armelle Brun. 2022. Being Diverse is Not Enough: Rethinking Diversity Evaluation to Meet Challenges of News Recommender Systems. In Adjunct Proceedings of the 30th ACM Conference on User Modeling, Adaptation and Personalization. 222–233.

Digital Library

[30]

Patti M Valkenburg, Jochen Peter, and Joseph B Walther. 2016. Media effects: Theory and research. Annual review of psychology 67 (2016), 315–338.

[31]

Saúl Vargas. 2011. New approaches to diversity and novelty in recommender systems. In Fourth BCS-IRSG Symposium on Future Directions in Information Access (FDIA 2011) 4. 8–13.

Digital Library

[32]

Sanne Vrijenhoek, Gabriel Bénédict, Mateo Gutierrez Granada, Daan Odijk, and Maarten De Rijke. 2022. RADio–Rank-Aware Divergence Metrics to Measure Normative Diversity in News Recommendations. In Proceedings of the 16th ACM Conference on Recommender Systems. 208–219.

Digital Library

Cited By

Heitz LVrijenhoek SInel O(2024)Recommendations for the Recommenders: Reflections on Prioritizing Diversity in the RecSys ChallengeProceedings of the Recommender Systems Challenge 202410.1145/3687151.3687155(22-26)Online publication date: 14-Oct-2024
https://dl.acm.org/doi/10.1145/3687151.3687155
Heitz LCroci JSachdeva MBernstein A(2024)Informfully - Research Platform for Reproducible User StudiesProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688066(660-669)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3688066
Starke ABremnes AKnudsen ETrilling DTrattner CBoratto LGena CMarras MGermanakos PPopescus E(2024)Perception versus Reality: Evaluating User Awareness of Political Selective Exposure in News Recommender SystemsAdjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization10.1145/3631700.3665189(286-291)Online publication date: 27-Jun-2024
https://dl.acm.org/doi/10.1145/3631700.3665189
Show More Cited By

Index Terms

Deliberative Diversity for News Recommendations: Operationalization and Experimental User Study
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems
    2. Users and interactive retrieval
      1. Personalization

Recommendations

A user-centric evaluation of context-aware recommendations for a mobile news service

Traditional recommender systems provide personal suggestions based on the user's preferences, without taking into account any additional contextual information, such as time or device type. The added value of contextual information for the ...
A fuzzy Bayesian approach to integrate user and item based collaborating filtering for enhanced recommendations
iiWAS '15: Proceedings of the 17th International Conference on Information Integration and Web-based Applications & Services

Memory-based collaborative filtering (CF) techniques have been widely implemented for predicting ratings to unseen items by aggregating ratings of similar users or items in recommender systems (RS). Usually, sufficient ratings from similar users or ...
A comparative study of heterogeneous item recommendations in social systems

While recommendation approaches exploiting different input sources have started to proliferate in the literature, an explicit study of the effect of the combination of heterogeneous inputs is still missing. On the other hand, in this context there are ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Conferences

RecSys '23: Proceedings of the 17th ACM Conference on Recommender Systems

September 2023

1406 pages

ISBN:9798400702419

DOI:10.1145/3604915

Copyright © 2023 Owner/Author.

This work is licensed under a Creative Commons Attribution International 4.0 License.

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 14 September 2023

Check for updates

Author Tags

Qualifiers

Short-paper
Research
Refereed limited

Conference

RecSys '23

Sponsor:

RecSys '23: Seventeenth ACM Conference on Recommender Systems

September 18 - 22, 2023

Singapore, Singapore

Acceptance Rates

Overall Acceptance Rate 254 of 1,295 submissions, 20%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

4
Total Citations
View Citations
639
Total Downloads

Downloads (Last 12 months)410
Downloads (Last 6 weeks)41

Reflects downloads up to 17 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Heitz LVrijenhoek SInel O(2024)Recommendations for the Recommenders: Reflections on Prioritizing Diversity in the RecSys ChallengeProceedings of the Recommender Systems Challenge 202410.1145/3687151.3687155(22-26)Online publication date: 14-Oct-2024
https://dl.acm.org/doi/10.1145/3687151.3687155
Heitz LCroci JSachdeva MBernstein A(2024)Informfully - Research Platform for Reproducible User StudiesProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688066(660-669)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3688066
Starke ABremnes AKnudsen ETrilling DTrattner CBoratto LGena CMarras MGermanakos PPopescus E(2024)Perception versus Reality: Evaluating User Awareness of Political Selective Exposure in News Recommender SystemsAdjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization10.1145/3631700.3665189(286-291)Online publication date: 27-Jun-2024
https://dl.acm.org/doi/10.1145/3631700.3665189
Mattis NMasur PMoeller Jvan Atteveldt W(2024)It ain't easy: using normatively motivated news diversification to facilitate policy support, tolerance, and political participationInformation, Communication & Society10.1080/1369118X.2024.2423892(1-18)Online publication date: 11-Nov-2024
https://doi.org/10.1080/1369118X.2024.2423892

View Options

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

HTML Format

View this article in HTML Format.

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

Media

Figures

Other

Tables

View Table of Contents