Keyword: diversity

Algorithmic News Diversity and Democratic Theory: Adding Agonism to the Mix

Sax, M.
Digital Journalism, vol. 10, iss. : 10, pp: 1650-1670, 2022

Abstract

The role news recommenders can play in stimulating news diversity is receiving increasing amounts of attention. Democratic theory plays an important role in this debate because it helps explain why news diversity is important and which kinds of news diversity should be pursued. In this article, I observe that the current literature on news recommenders and news diversity largely draws on a narrow set of theories of liberal and deliberative democracy. Another strand of democratic theory often referred to as ‘agonism’ is often ignored. This, I argue, is a mistake. Liberal and deliberative theories of democracy focus on the question of how political disagreements and conflicts can be resolved in a rational and legitimate manner. Agonism, to the contrary, stresses the ineradicability of conflict and the need to make conflict productive. This difference in thinking about the purpose of democratic politics can also lead to new ways of thinking about the value of news diversity and role algorithmic news recommenders should play in promoting it. The overall aim of the article is (re)introduce agonistic theory to the news recommender context and to argue that agonism deserves more serious attention.

agonism, algorithmic news recommenders, Democracy, diversity, Media law, news recommenders

Bibtex

Article{nokey, title = {Algorithmic News Diversity and Democratic Theory: Adding Agonism to the Mix}, author = {Sax, M.}, doi = {https://doi.org/10.1080/21670811.2022.2114919}, year = {2022}, date = {2022-09-14}, journal = {Digital Journalism}, volume = {10}, issue = {10}, pages = {1650-1670}, abstract = {The role news recommenders can play in stimulating news diversity is receiving increasing amounts of attention. Democratic theory plays an important role in this debate because it helps explain why news diversity is important and which kinds of news diversity should be pursued. In this article, I observe that the current literature on news recommenders and news diversity largely draws on a narrow set of theories of liberal and deliberative democracy. Another strand of democratic theory often referred to as ‘agonism’ is often ignored. This, I argue, is a mistake. Liberal and deliberative theories of democracy focus on the question of how political disagreements and conflicts can be resolved in a rational and legitimate manner. Agonism, to the contrary, stresses the ineradicability of conflict and the need to make conflict productive. This difference in thinking about the purpose of democratic politics can also lead to new ways of thinking about the value of news diversity and role algorithmic news recommenders should play in promoting it. The overall aim of the article is (re)introduce agonistic theory to the news recommender context and to argue that agonism deserves more serious attention.}, keywords = {agonism, algorithmic news recommenders, Democracy, diversity, Media law, news recommenders}, }

Recommenders with a Mission: Assessing Diversity in News Recommendations external link

Vrijenhoek, S., Kaya, M., Metoui, N., Möller, J., Odijk, D. & Helberger, N.
CHIIR '21: Proceedings of the 2021 Conference on Human Information Interaction and Retrieval, pp: 173-183, 2021

Abstract

News recommenders help users to find relevant online content and have the potential to fulfill a crucial role in a democratic society, directing the scarce attention of citizens towards the information that is most important to them. Simultaneously, recent concerns about so-called filter bubbles, misinformation and selective exposure are symptomatic of the disruptive potential of these digital news recommenders. Recommender systems can make or break filter bubbles, and as such can be instrumental in creating either a more closed or a more open internet. Current approaches to evaluating recommender systems are often focused on measuring an increase in user clicks and short-term engagement, rather than measuring the user's longer term interest in diverse and important information. This paper aims to bridge the gap between normative notions of diversity, rooted in democratic theory, and quantitative metrics necessary for evaluating the recommender system. We propose a set of metrics grounded in social science interpretations of diversity and suggest ways for practical implementations.

diversity, Mediarecht, news recommenders

Bibtex

Article{nokey, title = {Recommenders with a Mission: Assessing Diversity in News Recommendations}, author = {Vrijenhoek, S. and Kaya, M. and Metoui, N. and Möller, J. and Odijk, D. and Helberger, N.}, url = {https://dl.acm.org/doi/10.1145/3406522.3446019}, doi = {https://doi.org/10.1145/3406522.3446019}, year = {2021}, date = {2021-03-14}, journal = {CHIIR '21: Proceedings of the 2021 Conference on Human Information Interaction and Retrieval}, abstract = {News recommenders help users to find relevant online content and have the potential to fulfill a crucial role in a democratic society, directing the scarce attention of citizens towards the information that is most important to them. Simultaneously, recent concerns about so-called filter bubbles, misinformation and selective exposure are symptomatic of the disruptive potential of these digital news recommenders. Recommender systems can make or break filter bubbles, and as such can be instrumental in creating either a more closed or a more open internet. Current approaches to evaluating recommender systems are often focused on measuring an increase in user clicks and short-term engagement, rather than measuring the user\'s longer term interest in diverse and important information. This paper aims to bridge the gap between normative notions of diversity, rooted in democratic theory, and quantitative metrics necessary for evaluating the recommender system. We propose a set of metrics grounded in social science interpretations of diversity and suggest ways for practical implementations.}, keywords = {diversity, Mediarecht, news recommenders}, }

Study on media plurality and diversity online external link

Parcu, P.L., Brogi, E., Verza, S, Irion, K., Fahy, R., Idiz, D. R, Meiring, A., Seipp, T. & Poort, J.
2022

Abstract

The Study on Media Plurality and Diversity Online investigates the value of safeguarding media pluralism and diversity online, focusing on (i) the prominence and discoverability of general interest content and services, and on (ii) market plurality and the concentration of economic resources. With a focus on Europe, the project is funded by a tender from the European Commission to produce a study on Media Plurality and Diversity Online and involves four partner universities: CMPF (EUI); CiTiP (Centre for Information Technology and Intellectual Property) of KU Leuven; the Institute for Information Law of the University of Amsterdam (IViR/UvA); imec-SMIT-Vrije Universiteit Brussel. The purpose of the assignment was to describe, analyse and evaluate the existing regulatory and business practices in the two areas mentioned above, and finally to elaborate some policy recommendations. Data were collected from the database of the Media Pluralism Monitor (CMPF) and through desk research, online consultations and interviews with stakeholders. The contractor was able to call on a network of national experts across the Member States to support this work.

diversity, Media law, media plurality

Bibtex

Report{nokey, title = {Study on media plurality and diversity online}, author = {Parcu, P.L. and Brogi, E. and Verza, S and Irion, K. and Fahy, R. and Idiz, D. R and Meiring, A. and Seipp, T. and Poort, J.}, url = {https://data.europa.eu/doi/10.2759/529019}, doi = {https://doi.org/10.2759/529019}, year = {2022}, date = {2022-09-16}, abstract = {The Study on Media Plurality and Diversity Online investigates the value of safeguarding media pluralism and diversity online, focusing on (i) the prominence and discoverability of general interest content and services, and on (ii) market plurality and the concentration of economic resources. With a focus on Europe, the project is funded by a tender from the European Commission to produce a study on Media Plurality and Diversity Online and involves four partner universities: CMPF (EUI); CiTiP (Centre for Information Technology and Intellectual Property) of KU Leuven; the Institute for Information Law of the University of Amsterdam (IViR/UvA); imec-SMIT-Vrije Universiteit Brussel. The purpose of the assignment was to describe, analyse and evaluate the existing regulatory and business practices in the two areas mentioned above, and finally to elaborate some policy recommendations. Data were collected from the database of the Media Pluralism Monitor (CMPF) and through desk research, online consultations and interviews with stakeholders. The contractor was able to call on a network of national experts across the Member States to support this work.}, keywords = {diversity, Media law, media plurality}, }

Are we human, or are we users? The role of natural language processing in human-centric news recommenders that nudge users to diverse content external link

Reuver, M., Mattis, N., Sax, M., Verberne, S., Tintarev, N., Helberger, N., Müller, J., Vrijenhoek, S., Fokkens, A. & Van Atteveldt, W.
The 1st Workshop on NLP for Positive Impact: NLP4PosImpact 2021 : proceedings of the workshop, pp: 47-59, 2021

algorithmic news recommenders, diversity, diversity metrics

Bibtex

Article{Reuver2021, title = {Are we human, or are we users? The role of natural language processing in human-centric news recommenders that nudge users to diverse content}, author = {Reuver, M. and Mattis, N. and Sax, M. and Verberne, S. and Tintarev, N. and Helberger, N. and Müller, J. and Vrijenhoek, S. and Fokkens, A. and Van Atteveldt, W.}, url = {https://aclanthology.org/2021.nlp4posimpact-1.6/}, doi = {https://doi.org/https://doi.org/10.18653/v1/2021.nlp4posimpact-1.6}, year = {0801}, date = {2021-08-01}, journal = {The 1st Workshop on NLP for Positive Impact: NLP4PosImpact 2021 : proceedings of the workshop}, keywords = {algorithmic news recommenders, diversity, diversity metrics}, }

Diversity, Fairness, and Data-Driven Personalization in (News) Recommender System external link

Bernstein, A., Vreese, C.H. de, Helberger, N., Schulz, W. & Zweig, K.A.
Dagstuhl Reports, vol. 9, num: 11, pp: 117-124, 2020

Abstract

As people increasingly rely on online media and recommender systems to consume information, engage in debates and form their political opinions, the design goals of online media and news recommenders have wide implications for the political and social processes that take place online and offline. Current recommender systems have been observed to promote personalization and more effective forms of informing, but also to narrow the user’s exposure to diverse content. Concerns about echo-chambers and filter bubbles highlight the importance of design metrics that can successfully strike a balance between accurate recommendations that respond to individual information needs and preferences, while at the same time addressing concerns about missing out important information, context and the broader cultural and political diversity in the news, as well as fairness. A broader, more sophisticated vision of the future of personalized recommenders needs to be formed–a vision that can only be developed as the result of a collaborative effort by different areas of academic research (media studies, computer science, law and legal philosophy, communication science, political philosophy, and democratic theory). The proposed workshop will set first steps to develop such a much needed vision on the role of recommender systems on the democratic role of the media and define the guidelines as well as a manifesto for future research and long-term goals for the emerging topic of fairness, diversity, and personalization in recommender systems.

diversity, fairness, frontpage, Mediarecht, personalisatie, recommender systems

Bibtex

Article{Bernstein2020, title = {Diversity, Fairness, and Data-Driven Personalization in (News) Recommender System}, author = {Bernstein, A. and Vreese, C.H. de and Helberger, N. and Schulz, W. and Zweig, K.A.}, url = {https://www.ivir.nl/publicaties/download/dagrep_v009_i011_p117_19482.pdf}, doi = {https://doi.org/10.4230/DagRep.9.11.117}, year = {0402}, date = {2020-04-02}, journal = {Dagstuhl Reports}, volume = {9}, number = {11}, pages = {117-124}, abstract = {As people increasingly rely on online media and recommender systems to consume information, engage in debates and form their political opinions, the design goals of online media and news recommenders have wide implications for the political and social processes that take place online and offline. Current recommender systems have been observed to promote personalization and more effective forms of informing, but also to narrow the user’s exposure to diverse content. Concerns about echo-chambers and filter bubbles highlight the importance of design metrics that can successfully strike a balance between accurate recommendations that respond to individual information needs and preferences, while at the same time addressing concerns about missing out important information, context and the broader cultural and political diversity in the news, as well as fairness. A broader, more sophisticated vision of the future of personalized recommenders needs to be formed–a vision that can only be developed as the result of a collaborative effort by different areas of academic research (media studies, computer science, law and legal philosophy, communication science, political philosophy, and democratic theory). The proposed workshop will set first steps to develop such a much needed vision on the role of recommender systems on the democratic role of the media and define the guidelines as well as a manifesto for future research and long-term goals for the emerging topic of fairness, diversity, and personalization in recommender systems.}, keywords = {diversity, fairness, frontpage, Mediarecht, personalisatie, recommender systems}, }

Designing for the Better by Taking Users into Account: A Qualitative Evaluation of User Control Mechanisms in (News) Recommender Systems external link

Harambam, J., Bountouridis, D., Makhortykh, M. & van Hoboken, J.
RecSys'19: Proceedings of the 13th ACM Conference on Recommender Systems, pp: 69-77, 2019

Abstract

Recommender systems (RS) are on the rise in many domains. While they offer great promises, they also raise concerns: lack of transparency, reduction of diversity, little to no user control. In this paper, we align with the normative turn in computer science which scrutinizes the ethical and societal implications of RS. We focus and elaborate on the concept of user control because that mitigates multiple problems at once. Taking the news industry as our domain, we conducted four focus groups, or moderated think-aloud sessions, with Dutch news readers (N=21) to systematically study how people evaluate different control mechanisms (at the input, process, and output phase) in a News Recommender Prototype (NRP). While these mechanisms are sometimes met with distrust about the actual control they offer, we found that an intelligible user profile (including reading history and flexible preferences settings), coupled with possibilities to influence the recommendation algorithms is highly valued, especially when these control mechanisms can be operated in relation to achieving personal goals. By bringing (future) users' perspectives to the fore, this paper contributes to a richer understanding of why and how to design for user control in recommender systems.

diversity, filter bubble, frontpage, Mediarecht, recommender systems, Technologie en recht, Transparency

Bibtex

Article{Harambam2019b, title = {Designing for the Better by Taking Users into Account: A Qualitative Evaluation of User Control Mechanisms in (News) Recommender Systems}, author = {Harambam, J. and Bountouridis, D. and Makhortykh, M. and van Hoboken, J.}, url = {https://www.ivir.nl/publicaties/download/paper_recsys_19.pdf https://dl.acm.org/citation.cfm?id=3347014}, year = {0919}, date = {2019-09-19}, journal = {RecSys'19: Proceedings of the 13th ACM Conference on Recommender Systems}, abstract = {Recommender systems (RS) are on the rise in many domains. While they offer great promises, they also raise concerns: lack of transparency, reduction of diversity, little to no user control. In this paper, we align with the normative turn in computer science which scrutinizes the ethical and societal implications of RS. We focus and elaborate on the concept of user control because that mitigates multiple problems at once. Taking the news industry as our domain, we conducted four focus groups, or moderated think-aloud sessions, with Dutch news readers (N=21) to systematically study how people evaluate different control mechanisms (at the input, process, and output phase) in a News Recommender Prototype (NRP). While these mechanisms are sometimes met with distrust about the actual control they offer, we found that an intelligible user profile (including reading history and flexible preferences settings), coupled with possibilities to influence the recommendation algorithms is highly valued, especially when these control mechanisms can be operated in relation to achieving personal goals. By bringing (future) users\' perspectives to the fore, this paper contributes to a richer understanding of why and how to design for user control in recommender systems.}, keywords = {diversity, filter bubble, frontpage, Mediarecht, recommender systems, Technologie en recht, Transparency}, }

On the Democratic Role of News Recommenders external link

Helberger, N.
Digital Journalism, vol. 7, num: 8, pp: 993-1012, 2019

Abstract

Are algorithmic news recommenders a threat to the democratic role of the media? Or are they an opportunity, and, if so, how would news recommenders need to be designed to advance values and goals that we consider essential in a democratic society? These are central questions in the ongoing academic and policy debate about the likely implications of data analytics and machine learning for the democratic role of the media and the shift from traditional mass-media modes of distribution towards more personalised news and platforms Building on democratic theory and the growing body of literature about the digital turn in journalism, this article offers a conceptual framework for assessing the threats and opportunities around the democratic role of news recommenders, and develops a typology of different ‘democratic recommenders’.

AI public sphere, algorithmic news recommenders, democratic role of the media, democratic theories, diversity, frontpage, Mediarecht

Bibtex

Article{Helberger2019b, title = {On the Democratic Role of News Recommenders}, author = {Helberger, N.}, url = {https://www.tandfonline.com/doi/full/10.1080/21670811.2019.1623700}, doi = {https://doi.org/10.1080/21670811.2019.1623700}, year = {0628}, date = {2019-06-28}, journal = {Digital Journalism}, volume = {7}, number = {8}, pages = {993-1012}, abstract = {Are algorithmic news recommenders a threat to the democratic role of the media? Or are they an opportunity, and, if so, how would news recommenders need to be designed to advance values and goals that we consider essential in a democratic society? These are central questions in the ongoing academic and policy debate about the likely implications of data analytics and machine learning for the democratic role of the media and the shift from traditional mass-media modes of distribution towards more personalised news and platforms Building on democratic theory and the growing body of literature about the digital turn in journalism, this article offers a conceptual framework for assessing the threats and opportunities around the democratic role of news recommenders, and develops a typology of different ‘democratic recommenders’.}, keywords = {AI public sphere, algorithmic news recommenders, democratic role of the media, democratic theories, diversity, frontpage, Mediarecht}, }

Challenging Diversity – Social Media Platforms and a New Conception of Media Diversity external link

Helberger, N.
Oxford University Press, 0823, pp: 153-175, ISBN: 9780190845117

diversity, Mediarecht, Platforms, Social media

Bibtex

Chapter{Helberger2018b, title = {Challenging Diversity – Social Media Platforms and a New Conception of Media Diversity}, author = {Helberger, N.}, year = {0823}, date = {2018-08-23}, keywords = {diversity, Mediarecht, Platforms, Social media}, }