Team
Irina Nalis-Neuner
PostDoc Researcher
Dr. Irina Nalis, is a psychologist and joined the RecSys Lab as interdisciplinary collaborator. She obtained her PhD from the University of Vienna, department of Occupational, Economic, and Social Psychology. Her research interests are framed within the context of the Vienna Manifesto on Digital Humanism including questions of behavior change, co-creation, decision-making, and transformative research.
Teaching
- Bachelor Seminary in Psychology with interdisciplinary empirical research on Digital Humanism, University of Vienna, WS22, SS23
Activities & Talks
- Talk Open Arts – Academy of Fine Arts and The Open Innovation in Science Center of the Ludwig Boltzmann Gesellschaft, 2023
- Talk Opera Aperta Art & Digital Humanism, 2022
- Talk Digital Humanism ARS Akademie, 2022
Other
- Digital Humanism Summer School 2023 Group Projects on “Sustainable Solutions for Wicked Problems”
- Curator in the Discourse Programm of Elevate Festival Graz with a focus on digital, democracy and societal innovation
Publications
2026
Nalis, Irina; Klüwer, Nils; Neidhardt, Julia
From Labels to Context: Context-Sensitive User Modeling of Affect with Large Language Models Proceedings Article
In: Proceedings of the 34th ACM Conference on User Modeling, Adaptation and Personalization, pp. 490–493, Association for Computing Machinery, New York, NY, USA, 2026, ISBN: 9798400723117.
@inproceedings{10.1145/3774935.3812706,
title = {From Labels to Context: Context-Sensitive User Modeling of Affect with Large Language Models},
author = {Irina Nalis and Nils Klüwer and Julia Neidhardt},
url = {https://doi.org/10.1145/3774935.3812706},
doi = {10.1145/3774935.3812706},
isbn = {9798400723117},
year = {2026},
date = {2026-01-01},
booktitle = {Proceedings of the 34th ACM Conference on User Modeling, Adaptation and Personalization},
pages = {490–493},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
series = {UMAP '26},
abstract = {Emotion-aware user modeling is increasingly important in adaptive systems, yet many approaches still represent affect as a fixed label inferred from isolated inputs. This limits their ability to capture how emotional meaning develops across context, time, and interaction history. We address this limitation by modeling affect as a user-level, context-dependent phenomenon. We introduce the context sphere, a structured user representation that aggregates interaction history and preserves conversational and temporal context. Building on this representation, we use a guided two-stage Large Language Model (LLM) pipeline to generate affective interpretations over extended interaction context. We evaluate the approach using automated consistency checks and an exploratory qualitative study with five semi-structured interviews. Results provide initial evidence of plausible, interpretable context-sensitive affective interpretation.},
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Emotion-aware user modeling is increasingly important in adaptive systems, yet many approaches still represent affect as a fixed label inferred from isolated inputs. This limits their ability to capture how emotional meaning develops across context, time, and interaction history. We address this limitation by modeling affect as a user-level, context-dependent phenomenon. We introduce the context sphere, a structured user representation that aggregates interaction history and preserves conversational and temporal context. Building on this representation, we use a guided two-stage Large Language Model (LLM) pipeline to generate affective interpretations over extended interaction context. We evaluate the approach using automated consistency checks and an exploratory qualitative study with five semi-structured interviews. Results provide initial evidence of plausible, interpretable context-sensitive affective interpretation.
2025
Kolb, Thomas Elmar; Nalis, Irina; Neidhardt, Julia
Bridging Preferences: Multi-Stakeholder Insights on Ideal News Recommendations Proceedings Article
In: Proceedings of the 33rd ACM Conference on User Modeling, Adaptation and Personalization, pp. 268–272, Association for Computing Machinery, New York, NY, USA, 2025, ISBN: 9798400713132.
@inproceedings{10.1145/3699682.3728355,
title = {Bridging Preferences: Multi-Stakeholder Insights on Ideal News Recommendations},
author = {Thomas Elmar Kolb and Irina Nalis and Julia Neidhardt},
url = {https://doi.org/10.1145/3699682.3728355},
doi = {10.1145/3699682.3728355},
isbn = {9798400713132},
year = {2025},
date = {2025-01-01},
booktitle = {Proceedings of the 33rd ACM Conference on User Modeling, Adaptation and Personalization},
pages = {268–272},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
series = {UMAP '25},
abstract = {In the evolving realm of recommender systems, our study contributes to the understanding of potential improvements in news recommendation beyond accuracy. Central to our research is the integration of insights from news industry experts and prospective readers, compared with automated news recommendations. We conducted a labeling study with 168 articles, using Best-Worst Scaling (BWS) for ranking and topic modeling. This approach enabled a thorough examination of stakeholder expectations for ideal reading recommendations, specifically by investigating the gap between stated and revealed preferences. Our findings show alignment in ranking behavior among journalists, prospective readers, and the BM-25 algorithm. However, preferences for different beyond-accuracy measures varied. Accompanying this work, a corpus of news articles and the labeled rankings have been made available.},
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In the evolving realm of recommender systems, our study contributes to the understanding of potential improvements in news recommendation beyond accuracy. Central to our research is the integration of insights from news industry experts and prospective readers, compared with automated news recommendations. We conducted a labeling study with 168 articles, using Best-Worst Scaling (BWS) for ranking and topic modeling. This approach enabled a thorough examination of stakeholder expectations for ideal reading recommendations, specifically by investigating the gap between stated and revealed preferences. Our findings show alignment in ranking behavior among journalists, prospective readers, and the BM-25 algorithm. However, preferences for different beyond-accuracy measures varied. Accompanying this work, a corpus of news articles and the labeled rankings have been made available.
Klüwer, Nils; Nalis, Irina; Neidhardt, Julia
Context over Categories: Implementing the Theory of Constructed Emotion with LLM-Guided User Analysis Proceedings Article
In: Proceedings of the Extended Abstracts of the CHI Conference on Human Factors in Computing Systems, Association for Computing Machinery, New York, NY, USA, 2025, ISBN: 9798400713958.
@inproceedings{10.1145/3706599.3721205,
title = {Context over Categories: Implementing the Theory of Constructed Emotion with LLM-Guided User Analysis},
author = {Nils Klüwer and Irina Nalis and Julia Neidhardt},
url = {https://doi.org/10.1145/3706599.3721205},
doi = {10.1145/3706599.3721205},
isbn = {9798400713958},
year = {2025},
date = {2025-01-01},
booktitle = {Proceedings of the Extended Abstracts of the CHI Conference on Human Factors in Computing Systems},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
series = {CHI EA '25},
abstract = {Emotion analysis is a critical research area with applications in content moderation and personalized systems. Many existing approaches rely on Ekman’s universal emotions theory, which reduces emotions to static categories, neglecting their complexity and contextual variability. This work introduces a novel, context-aware approach based on Lisa Feldman Barrett’s Theory of Constructed Emotion. A key contribution is the development of the “context sphere,” a personalized construct derived from user behavior data. To our knowledge, this is the first operationalization for computational methods. A context-aware emotion analysis pipeline was developed, incorporating advanced Large Language Model (LLM) prompting strategies like role-play and controlled generation. A case study in content moderation demonstrates how the “context sphere” enables contextually aware emotion analyses. Future directions include refining the framework, advancing LLM methodologies, and conducting user studies. This research lays the foundation for more human-centered, ethical, and effective emotion analysis systems.},
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Emotion analysis is a critical research area with applications in content moderation and personalized systems. Many existing approaches rely on Ekman’s universal emotions theory, which reduces emotions to static categories, neglecting their complexity and contextual variability. This work introduces a novel, context-aware approach based on Lisa Feldman Barrett’s Theory of Constructed Emotion. A key contribution is the development of the “context sphere,” a personalized construct derived from user behavior data. To our knowledge, this is the first operationalization for computational methods. A context-aware emotion analysis pipeline was developed, incorporating advanced Large Language Model (LLM) prompting strategies like role-play and controlled generation. A case study in content moderation demonstrates how the “context sphere” enables contextually aware emotion analyses. Future directions include refining the framework, advancing LLM methodologies, and conducting user studies. This research lays the foundation for more human-centered, ethical, and effective emotion analysis systems.
2024
Nalis, Irina; Sippl, Tobias; Kolb, Thomas Elmar; Neidhardt, Julia
Navigating Serendipity – An Experimental User Study On The Interplay of Trust and Serendipity In Recommender Systems Proceedings Article
In: Adjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization, pp. 386–393, Association for Computing Machinery, Cagliari, Italy, 2024, ISBN: 9798400704666.
@inproceedings{10.1145/3631700.3664901,
title = {Navigating Serendipity - An Experimental User Study On The Interplay of Trust and Serendipity In Recommender Systems},
author = {Irina Nalis and Tobias Sippl and Thomas Elmar Kolb and Julia Neidhardt},
url = {https://doi.org/10.1145/3631700.3664901},
doi = {10.1145/3631700.3664901},
isbn = {9798400704666},
year = {2024},
date = {2024-01-01},
booktitle = {Adjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization},
pages = {386–393},
publisher = {Association for Computing Machinery},
address = {Cagliari, Italy},
series = {UMAP Adjunct '24},
abstract = {Recommender systems play a crucial role in our daily lives, constantly evolving to meet the diverse needs of users. As the pursuit of improved user experiences continues, metrics such as serendipity have emerged within the realm of beyond-accuracy paradigms. However, integrating serendipitous recommendations presents complex challenges, necessitating a delicate balance between novelty, relevance, and user engagement. In this interdisciplinary experimental user study, we address these challenges within the context of a book recommender system. By investigating the impact of interface design changes on user trust, a key determinant of satisfaction with serendipitous recommendations, we measured trust levels for both individual recommended items and the recommender system as a whole. Our findings indicate that while interface enhancements did not yield significant increases in trust, they did notably elevate serendipity ratings for previously unknown books. These results highlight the intricate interplay between technical and psychological factors in the design of recommender systems, emphasizing the importance of human-centered approaches in the creation of more responsible AI applications. This research contributes to ongoing discussions surrounding user-centric recommendation systems and aligns with broader themes of digital humanism and responsible AI.},
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Recommender systems play a crucial role in our daily lives, constantly evolving to meet the diverse needs of users. As the pursuit of improved user experiences continues, metrics such as serendipity have emerged within the realm of beyond-accuracy paradigms. However, integrating serendipitous recommendations presents complex challenges, necessitating a delicate balance between novelty, relevance, and user engagement. In this interdisciplinary experimental user study, we address these challenges within the context of a book recommender system. By investigating the impact of interface design changes on user trust, a key determinant of satisfaction with serendipitous recommendations, we measured trust levels for both individual recommended items and the recommender system as a whole. Our findings indicate that while interface enhancements did not yield significant increases in trust, they did notably elevate serendipity ratings for previously unknown books. These results highlight the intricate interplay between technical and psychological factors in the design of recommender systems, emphasizing the importance of human-centered approaches in the creation of more responsible AI applications. This research contributes to ongoing discussions surrounding user-centric recommendation systems and aligns with broader themes of digital humanism and responsible AI.
Nalis, Irina; Neidhardt, Julia
Towards Possibility: Interdisciplinary Perspectives on Enhancing Recommender Systems Beyond Accuracy Miscellaneous
2024.
@misc{ 20.500.12708_210789,
title = {Towards Possibility: Interdisciplinary Perspectives on Enhancing Recommender Systems Beyond Accuracy},
author = {Irina Nalis and Julia Neidhardt},
year = {2024},
date = {2024-01-01},
abstract = {In this introductory lecture, Dr. Julia Neidhardt, Director of the CDLab for Recommender Systems and UNESCO Co-Chair in Digital Humanism, and Dr. Irina Nalis, psychologist, and interdisciplinary researcher at the CDLab, explore recommender systems from a Digital Humanism viewpoint, focusing on the intersection of technology, psychology, and societal needs. Addressing the limitations and risks of accuracy-centric metrics, they emphasize the importance of establishing new research and development methods to go beyond accuracy and towards more human-potential centered recommendations. Their lecture, based on interdisciplinary research including advanced algorithms, cross-domain recommendations, and the integration of large-language models, demonstrates the potential of recommender systems to foster diversity, serendipity, and democratic fairness. They further discuss the role of choice architecture and affordances in making responsible recommendations, highlighting the importance of aligning with broader policies like the EU Digital Services Act to meet societal needs and enrich the digital landscape.},
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In this introductory lecture, Dr. Julia Neidhardt, Director of the CDLab for Recommender Systems and UNESCO Co-Chair in Digital Humanism, and Dr. Irina Nalis, psychologist, and interdisciplinary researcher at the CDLab, explore recommender systems from a Digital Humanism viewpoint, focusing on the intersection of technology, psychology, and societal needs. Addressing the limitations and risks of accuracy-centric metrics, they emphasize the importance of establishing new research and development methods to go beyond accuracy and towards more human-potential centered recommendations. Their lecture, based on interdisciplinary research including advanced algorithms, cross-domain recommendations, and the integration of large-language models, demonstrates the potential of recommender systems to foster diversity, serendipity, and democratic fairness. They further discuss the role of choice architecture and affordances in making responsible recommendations, highlighting the importance of aligning with broader policies like the EU Digital Services Act to meet societal needs and enrich the digital landscape.
2023
Nalis, Irina; Neidhardt, Julia
Not Facial Expression, nor Fingerprint – Acknowledging Complexity and Context in Emotion Research for Human-Centered Personalization and Adaptation Proceedings Article
In: Adjunct Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization, pp. 325–330, Association for Computing Machinery, New York, 2023.
@inproceedings{ 20.500.12708_192197,
title = {Not Facial Expression, nor Fingerprint – Acknowledging Complexity and Context in Emotion Research for Human-Centered Personalization and Adaptation},
author = {Irina Nalis and Julia Neidhardt},
doi = {10.1145/3563359.3596990},
year = {2023},
date = {2023-01-01},
booktitle = {Adjunct Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization},
pages = {325–330},
publisher = {Association for Computing Machinery},
address = {New York},
abstract = {While research on emotion has emerged as a crucial area in studying this relationship, the use of classical psychological concepts in human emotion detection and sentiment analysis has been challenged by the cognitive sciences and psychology. This paper argues that the uncritical adoption of concepts that overlook the complexity and context of emotions may hinder progress in this field. To overcome this limitation, the theory of constructed emotion is reviewed, which suggests that emotions are not distinct categories but rather dimensions that require dynamic, rather than static, contextualized models. By prioritizing digital wellbeing in emotion studies and acknowledging complexity and context, future research can develop more effective models for emotion detection and sentiment analysis. The aim is to provide valuable insights for researchers seeking to advance our understanding of the relationship between technology and wellbeing for human centered-adaptation and personalization.},
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While research on emotion has emerged as a crucial area in studying this relationship, the use of classical psychological concepts in human emotion detection and sentiment analysis has been challenged by the cognitive sciences and psychology. This paper argues that the uncritical adoption of concepts that overlook the complexity and context of emotions may hinder progress in this field. To overcome this limitation, the theory of constructed emotion is reviewed, which suggests that emotions are not distinct categories but rather dimensions that require dynamic, rather than static, contextualized models. By prioritizing digital wellbeing in emotion studies and acknowledging complexity and context, future research can develop more effective models for emotion detection and sentiment analysis. The aim is to provide valuable insights for researchers seeking to advance our understanding of the relationship between technology and wellbeing for human centered-adaptation and personalization.
Modre, Laura; Neidhardt, Julia; Nalis, Irina
Value-Based Nudging in News Recommender Systems – Results From an Experimental User Study Proceedings Article
In: Proceedings of the First Workshop on the Normative Design and Evaluation of Recommender Systems (NORMalize 2023), 2023.
@inproceedings{modre2023value,
title = {Value-Based Nudging in News Recommender Systems – Results From an Experimental User Study},
author = {Laura Modre and Julia Neidhardt and Irina Nalis},
url = {https://ceur-ws.org/Vol-3639/paper4.pdf},
year = {2023},
date = {2023-01-01},
urldate = {2023-01-01},
booktitle = {Proceedings of the First Workshop on the Normative Design and Evaluation of Recommender Systems (NORMalize 2023)},
volume = {3639},
series = {CEUR Workshop Proceedings},
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Kolb, Thomas Elmar; Nalis-Neuner, Irina; Neidhardt, Julia
Like a Skilled DJ – an Expert Study on News Recommendations Beyond Accuracy Proceedings Article
In: Kille, Benjamin (Ed.): CEUR-WS.org, 2023.
@inproceedings{ 20.500.12708_191170,
title = {Like a Skilled DJ - an Expert Study on News Recommendations Beyond Accuracy},
author = {Thomas Elmar Kolb and Irina Nalis-Neuner and Julia Neidhardt},
editor = {Benjamin Kille},
doi = {10.34726/5332},
year = {2023},
date = {2023-01-01},
volume = {3561},
publisher = {CEUR-WS.org},
series = {CEUR Workshop Proceedings},
abstract = {In the past, recommender systems were primarily focused on optimizing accuracy. However, in recent years, there has been an increasing awareness that considerations beyond accuracy are necessary. The definition of what constitutes a good recommendation is a crucial issue. The most precise prediction may not always be the recommendation that satisfies the user best. This study offers a comprehensive investigation into the present advancements within the realm of beyond-accuracy measurements, especially the metrics diversity, serendipity, and novelty. Collaborative efforts between algorithmic models and domain experts can enrich recommendation quality, particularly in labeling and categorizing content. To address this, we present a study conducted by experts in the news domain. This study provides new insights into the multifaceted nature of this challenge. Employing an interdisciplinary approach, we underscore the significance of constructing a system that revolves around the user. Recent discussions about algorithmic content filtering and its societal implications underscore the importance of maintaining human involvement in the decision-making loop.},
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In the past, recommender systems were primarily focused on optimizing accuracy. However, in recent years, there has been an increasing awareness that considerations beyond accuracy are necessary. The definition of what constitutes a good recommendation is a crucial issue. The most precise prediction may not always be the recommendation that satisfies the user best. This study offers a comprehensive investigation into the present advancements within the realm of beyond-accuracy measurements, especially the metrics diversity, serendipity, and novelty. Collaborative efforts between algorithmic models and domain experts can enrich recommendation quality, particularly in labeling and categorizing content. To address this, we present a study conducted by experts in the news domain. This study provides new insights into the multifaceted nature of this challenge. Employing an interdisciplinary approach, we underscore the significance of constructing a system that revolves around the user. Recent discussions about algorithmic content filtering and its societal implications underscore the importance of maintaining human involvement in the decision-making loop.
Basso, Linda; Nalis-Neuner, Irina; Neidhardt, Julia
News Diversity and Well-Being – An Experimental Exploration Of Diversity-Aware Recommender Systems Proceedings Article
In: 2023.
@inproceedings{ 20.500.12708_193212,
title = {News Diversity and Well-Being – An Experimental Exploration Of Diversity-Aware Recommender Systems},
author = {Linda Basso and Irina Nalis-Neuner and Julia Neidhardt},
year = {2023},
date = {2023-01-01},
abstract = {The demand for socially responsible designs for news recommender systems is currently of the utmost relevance. This paper presents a novel and interdisciplinary approach, bringing together psychol- ogists and computer scientists, to examine the impact of diverse news recommendations on individual users. In this experimental study, participants were divided into two groups, interacting with either a diverse news recommender system (experimental group) or a non-diversified system (control group). Subjective well-being and personal evaluations of the recommender system were measured. Although the study did not find a significant positive impact on participants’ subjective well-being after consuming more diverse news, this preliminary investigation opens avenues for further re- search. This study sets the stage for future investigations, providing valuable insights and highlighting the complexities of promoting diverse news consumption through recommender systems. Further research is warranted to explore potential enhancements and refine the understanding of the relationship between diversified news recommendations and user well-being. This contribution lays a groundstone for further research on responsibilities and how to implement basic human values, which are important to sustain and advance the democratic society we live in.},
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The demand for socially responsible designs for news recommender systems is currently of the utmost relevance. This paper presents a novel and interdisciplinary approach, bringing together psychol- ogists and computer scientists, to examine the impact of diverse news recommendations on individual users. In this experimental study, participants were divided into two groups, interacting with either a diverse news recommender system (experimental group) or a non-diversified system (control group). Subjective well-being and personal evaluations of the recommender system were measured. Although the study did not find a significant positive impact on participants’ subjective well-being after consuming more diverse news, this preliminary investigation opens avenues for further re- search. This study sets the stage for future investigations, providing valuable insights and highlighting the complexities of promoting diverse news consumption through recommender systems. Further research is warranted to explore potential enhancements and refine the understanding of the relationship between diversified news recommendations and user well-being. This contribution lays a groundstone for further research on responsibilities and how to implement basic human values, which are important to sustain and advance the democratic society we live in.
Knees, Peter; Neidhardt, Julia; Nalis-Neuner, Irina
Recommender Systems: Techniques, Effects, and Measures Toward Pluralism and Fairness Book Section
In: Werthner, Hannes; Ghezzi, Carlo; Kramer, Jeff (Ed.): pp. 417–434, Springer, Cham, 2023.
@incollection{ 20.500.12708_191188,
title = {Recommender Systems: Techniques, Effects, and Measures Toward Pluralism and Fairness},
author = {Peter Knees and Julia Neidhardt and Irina Nalis-Neuner},
editor = {Hannes Werthner and Carlo Ghezzi and Jeff Kramer},
doi = {10.1007/978-3-031-45304-5_27},
year = {2023},
date = {2023-01-01},
pages = {417–434},
publisher = {Springer},
address = {Cham},
abstract = {Recommender systems are widely used in various applications, such as online shopping, social media, and news personalization. They can help systems by delivering only the most relevant and promising information to their users and help people by mitigating information overload. At the same time, algorithmic recommender systems are a new form of gatekeeper that preselects and controls the information being presented and actively shapes users’ choices and behavior. This becomes a crucial aspect, as, if unaddressed and not safeguarded, these systems are susceptible to perpetuate and even amplify existing biases, including unwanted societal biases, leading to unfair and discriminatory outcomes. In this chapter, we briefly introduce recommender systems, their basic mechanisms, and their importance in various applications. We show how their outcomes and performance are assessed and discuss approaches to addressing pluralism and fairness in recommender systems. Finally, we highlight recently emerging directions within recommender systems research, pointing out opportunities for digital humanism to contribute interdisciplinary expertise.},
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Recommender systems are widely used in various applications, such as online shopping, social media, and news personalization. They can help systems by delivering only the most relevant and promising information to their users and help people by mitigating information overload. At the same time, algorithmic recommender systems are a new form of gatekeeper that preselects and controls the information being presented and actively shapes users’ choices and behavior. This becomes a crucial aspect, as, if unaddressed and not safeguarded, these systems are susceptible to perpetuate and even amplify existing biases, including unwanted societal biases, leading to unfair and discriminatory outcomes. In this chapter, we briefly introduce recommender systems, their basic mechanisms, and their importance in various applications. We show how their outcomes and performance are assessed and discuss approaches to addressing pluralism and fairness in recommender systems. Finally, we highlight recently emerging directions within recommender systems research, pointing out opportunities for digital humanism to contribute interdisciplinary expertise.
Nalis-Neuner, Irina
Digital Humanism for Sustainable Solutions to Wicked Problems: A Workshop Exploring Collective Intelligence Design Miscellaneous
2023.
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title = {Digital Humanism for Sustainable Solutions to Wicked Problems: A Workshop Exploring Collective Intelligence Design},
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Nalis-Neuner, Irina
Sentiment Analysis – Psychological Perspective : Tracing the Evolution of Emotion Studies in Psychology and Sentiment Analysis Miscellaneous
2023.
@misc{<LineBreak> 20.500.12708_193816,
title = {Sentiment Analysis - Psychological Perspective : Tracing the Evolution of Emotion Studies in Psychology and Sentiment Analysis},
author = {Irina Nalis-Neuner},
year = {2023},
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2022
Kolb, Thomas Elmar; Nalis, Irina; Sertkan, Mete; Neidhardt, Julia
The Role of Bias in News Recommendation in the Perception of the Covid-19 Pandemic Proceedings Article
In: Thomas, Kolb (Ed.): 2022.
@inproceedings{ 20.500.12708_150340,
title = {The Role of Bias in News Recommendation in the Perception of the Covid-19 Pandemic},
author = {Thomas Elmar Kolb and Irina Nalis and Mete Sertkan and Julia Neidhardt},
editor = {Kolb Thomas},
doi = {10.48550/ARXIV.2209.07608},
year = {2022},
date = {2022-01-01},
abstract = {News recommender systems (NRs) have been shown to shape public discourse and to enforce behaviors that have a critical, oftentimes detrimental effect on democracies. Earlier research on the impact of media bias has revealed their strong impact on opinions and preferences. Responsible NRs are supposed to have depolarizing capacities, once they go beyond accuracy measures. We performed sequence prediction by using the BERT4Rec algorithm to investigate the interplay of news of coverage and user behavior. Based on live data and training of a large data set from one news outlet "event bursts", "rally around the flag" effect and "filter bubbles" were investigated in our interdisciplinary approach between data science and psychology. Potentials for fair NRs that go beyond accuracy measures are outlined via training of the models with a large data set of articles, keywords, and user behavior. The development of the news coverage and user behavior of the COVID-19 pandemic from primarily medical to broader political content and debates was traced. Our study provides first insights for future development of responsible news recommendation that acknowledges user preferences while stimulating diversity and accountability instead of accuracy, only.},
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News recommender systems (NRs) have been shown to shape public discourse and to enforce behaviors that have a critical, oftentimes detrimental effect on democracies. Earlier research on the impact of media bias has revealed their strong impact on opinions and preferences. Responsible NRs are supposed to have depolarizing capacities, once they go beyond accuracy measures. We performed sequence prediction by using the BERT4Rec algorithm to investigate the interplay of news of coverage and user behavior. Based on live data and training of a large data set from one news outlet “event bursts”, “rally around the flag” effect and “filter bubbles” were investigated in our interdisciplinary approach between data science and psychology. Potentials for fair NRs that go beyond accuracy measures are outlined via training of the models with a large data set of articles, keywords, and user behavior. The development of the news coverage and user behavior of the COVID-19 pandemic from primarily medical to broader political content and debates was traced. Our study provides first insights for future development of responsible news recommendation that acknowledges user preferences while stimulating diversity and accountability instead of accuracy, only.