Keyword: news recommenders

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}, }

Regulation of news recommenders in the Digital Services Act: empowering David against the Very Large Online Goliath external link

Helberger, N., Drunen, M. van, Vrijenhoek, S. & Möller, J.
Internet Policy Review, 2021

Digital services act, frontpage, Mediarecht, news recommenders, Regulering

Bibtex

Article{Helberger2021b, title = {Regulation of news recommenders in the Digital Services Act: empowering David against the Very Large Online Goliath}, author = {Helberger, N. and Drunen, M. van and Vrijenhoek, S. and Möller, J.}, url = {https://policyreview.info/articles/news/regulation-news-recommenders-digital-services-act-empowering-david-against-very-large}, year = {0226}, date = {2021-02-26}, journal = {Internet Policy Review}, keywords = {Digital services act, frontpage, Mediarecht, news recommenders, Regulering}, }

News Recommenders and Cooperative Explainability: Confronting the contextual complexity in AI explanations external link

Drunen, M. van, Ausloos, J., Appelman, N. & Helberger, N.
2020

ai, frontpage, news recommenders, Technologie en recht

Bibtex

Report{Drunen2020b, title = {News Recommenders and Cooperative Explainability: Confronting the contextual complexity in AI explanations}, author = {Drunen, M. van and Ausloos, J. and Appelman, N. and Helberger, N.}, url = {https://www.ivir.nl/publicaties/download/Visiepaper-explainable-AI-final.pdf}, year = {1103}, date = {2020-11-03}, keywords = {ai, frontpage, news recommenders, Technologie en recht}, }