Team

@inproceedings{kolb2026exploring,

title = {Exploring Revealed Behavior and Stated Profiles in Longitudinal News Recommendation},

author = {Thomas Elmar Kolb and Alain Dominique Starke and Christoph Trattner},

year  = {2026},

date = {2026-09-01},

urldate = {2026-09-01},

booktitle = {Proceedings of the 20th ACM Conference on Recommender Systems (RecSys '26)},

publisher = {Association for Computing Machinery},

address = {New York, NY, USA},

keywords = {},

pubstate = {forthcoming},

tppubtype = {inproceedings}

}

@inproceedings{neidhardt2026when,

title = {When Should Recommender Systems Not Act?},

author = {Julia Neidhardt and Thomas Elmar Kolb and Ahmadou Wagne and Gwendolyn Rippberger and Ricardo Baeza-Yates},

year  = {2026},

date = {2026-09-01},

urldate = {2026-09-01},

booktitle = {Proceedings of the 20th ACM Conference on Recommender Systems (RecSys '26)},

publisher = {Association for Computing Machinery},

address = {New York, NY, USA},

keywords = {},

pubstate = {forthcoming},

tppubtype = {inproceedings}

}

@inproceedings{Modre2026,

title = {Interaction Modality and Trust: Investigating System-Driven Conversational Recommendation and Faceted Search},

author = {Laura Modre and Ahmadou Wagne and Thomas Elmar Kolb and Julia Neidhardt},

year  = {2026},

date = {2026-08-24},

booktitle = {Proceedings of the 20th ACM Conference on Recommender Systems},

publisher = {Association for Computing Machinery},

address = {New York, NY, USA},

keywords = {},

pubstate = {forthcoming},

tppubtype = {inproceedings}

}

@inproceedings{10.1145/3742414.3795097,

title = {Prompt to Press: Evaluating Human Perception of AI Involvement in News Writing Across Prompt Specificity},

author = {Uroš Sergaš and Ahmadou Wagne and Thomas Elmar Kolb and Julia Neidhardt and Bruce Ferwerda and Marko Tkalcic},

url = {https://doi.org/10.1145/3742414.3795097},

doi = {10.1145/3742414.3795097},

isbn = {9798400719851},

year  = {2026},

date = {2026-01-01},

booktitle = {Companion Proceedings of the 31st International Conference on Intelligent User Interfaces},

pages = {89–92},

publisher = {Association for Computing Machinery},

address = {New York, NY, USA},

series = {IUI '26 Companion},

abstract = {Large language models (LLMs) are becoming a common feature in content creation tools, prompting important questions about how design choices influence user trust and engagement in AI-assisted journalism. Beyond output quality, factors such as prompt specificity, model choice, and authorship disclosure are themselves interaction design parameters that influence how users interpret and evaluate AI contributions. Yet, little is known about how these design decisions affect reader perceptions in journalistic contexts. To address this gap, we conducted an experiment with 150 participants who evaluated news articles on the sensitive topic of assisted suicide. The articles systematically varied in authorship (human-written, AI-edited, or AI-generated), stance (pro- or anti-legalization), and prompt specificity (vague, moderate, or highly detailed). Participants rated each article on engagement, subjectivity, and perceived AI involvement, and also provided open-ended justifications for their authorship judgments. Our findings show that prompt specificity and model choice significantly influence perceptions of authorship, underscoring how technical design decisions in AI tools can shape public trust in journalism.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{10.1145/3774935.3802536,

title = {LLM4Good: The 2nd Workshop on Sustainable and Trustworthy Large Language Models for Personalization},

author = {Ahmadou Wagne and Thomas Elmar Kolb and Ashmi Banerjee and Julia Neidhardt and Yashar Deldjoo},

url = {https://doi.org/10.1145/3774935.3802536},

doi = {10.1145/3774935.3802536},

isbn = {9798400723117},

year  = {2026},

date = {2026-01-01},

booktitle = {Proceedings of the 34th ACM Conference on User Modeling, Adaptation and Personalization},

pages = {684–687},

publisher = {Association for Computing Machinery},

address = {New York, NY, USA},

series = {UMAP '26},

abstract = {Large Language Models (LLMs) are transforming personalized services by enabling adaptive, context-aware recommendations and interactions. However, deploying these models at scale raises significant concerns about environmental impact, fairness, privacy, and trustworthiness, including high energy consumption, biased outputs, privacy breaches, and hallucinations. The LLM4Good workshop was already hosted at UMAP’251 and is a half-day workshop that addresses these challenges by fostering dialogue on sustainable and ethical approaches to LLM-based personalization. Participants will explore energy-efficient techniques, bias mitigation, privacy-preserving methods, and responsible deployment strategies. The workshop aligns with Sustainable Development Goals and Digital Humanism principles. It aims to guide the development of trustworthy, human-centric LLM systems that positively impact education, healthcare, and other domains.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{10.1145/3708319.3733694,

title = {Beyond Demographics: Evaluating News Recommender Systems Fairness Through Behavioural Communities},

author = {Bernhard Steindl and Thomas Elmar Kolb and Julia Neidhardt},

url = {https://doi.org/10.1145/3708319.3733694},

doi = {10.1145/3708319.3733694},

isbn = {9798400713996},

year  = {2025},

date = {2025-01-01},

urldate = {2025-01-01},

booktitle = {Adjunct Proceedings of the 33rd ACM Conference on User Modeling, Adaptation and Personalization},

pages = {13–17},

publisher = {Association for Computing Machinery},

address = {New York, NY, USA},

series = {UMAP Adjunct '25},

abstract = {Fairness in recommender systems is often framed around demographic attributes. In this work, we explore a novel direction—evaluating fairness across latent behavioural communities derived from user interactions on a real-world news platform. Using graph-based community detection (Louvain and Infomap), we identify large user groups and examine how different network modelling choices affect fairness outcomes in both traditional and fairness-aware recommender systems. Experiments on an Austrian news dataset reveal that small changes in graph construction considerably impact community formation and recommendation quality. Notably, fairness-aware algorithms show only marginal improvements over standard approaches, underscoring the complexity of achieving equitable outcomes in real-world systems and raising important questions for future research.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{10.1145/3705328.3748010,

title = {A Tutorial on Recent Advances in Generative Conversational Recommender Systems},

author = {Thomas Elmar Kolb and Ahmadou Wagne and Ashmi Banerjee and Fatemeh Nazary and Julia Neidhardt and Yashar Deldjoo and Tommaso Di Noia},

url = {https://doi.org/10.1145/3705328.3748010},

doi = {10.1145/3705328.3748010},

isbn = {9798400713644},

year  = {2025},

date = {2025-01-01},

booktitle = {Proceedings of the Nineteenth ACM Conference on Recommender Systems},

pages = {1420–1422},

publisher = {Association for Computing Machinery},

address = {New York, NY, USA},

series = {RecSys '25},

abstract = {Conversational recommender systems (CRSs) are increasingly vital for delivering multi-turn, context-aware recommendations. This tutorial provides a concise yet comprehensive exploration of modern generative CRSs, highlighting recent advances in generative AI—such as breakthroughs in large language models and neural generation pipelines, that enhance dialogue management, user modeling, and response generation. In addition, the tutorial addresses core challenges, including data acquisition, multi-turn personalization, and evaluation issues, such as controlling hallucinations, accounting for social factors, and managing ethical considerations, while also discussing emerging risks and novel solutions. Ultimately, participants will be equipped with actionable insights and practical tools for building new conversational recommender systems powered by generative models.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@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.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{10.1145/3708319.3727560,

title = {LLM4Good: The 1st Workshop on Sustainable and Trustworthy Large Language Models for Personalization},

author = {Thomas Elmar Kolb and Ashmi Banerjee and Ahmadou Wagne and Julia Neidhardt and Yashar Deldjoo},

url = {https://doi.org/10.1145/3708319.3727560},

doi = {10.1145/3708319.3727560},

isbn = {9798400713996},

year  = {2025},

date = {2025-01-01},

booktitle = {Adjunct Proceedings of the 33rd ACM Conference on User Modeling, Adaptation and Personalization},

pages = {385–387},

publisher = {Association for Computing Machinery},

address = {New York, NY, USA},

series = {UMAP Adjunct '25},

abstract = {Large Language Models (LLMs) are transforming personalized services by enabling adaptive, context-aware recommendations and interactions. However, deploying these models at scale raises significant concerns about environmental impact, fairness, privacy, and trustworthiness, including high energy consumption, biased outputs, privacy breaches, and hallucinations. The LLM4Good workshop is a half-day workshop that addresses these challenges by fostering dialogue on sustainable and ethical approaches to LLM-based personalization. Participants will explore energy-efficient techniques, bias mitigation, privacy-preserving methods, and responsible deployment strategies. The workshop aligns with Sustainable Development Goals and Digital Humanism principles. It aims to guide the development of trustworthy, human-centric LLM systems that positively impact education, healthcare, and other domains.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Wagne_Neidhardt_Kolb_2024,

title = {PopAut: An Annotated Corpus for Populism Detection in Austrian News Comments},

author = {Ahmadou Wagne and Julia Neidhardt and Thomas Elmar Kolb},

editor = {Nicoletta Calzolari and Min-Yen Kan and Veronique Hoste and Alessandro Lenci and Sakriani Sakti and Nianwen Xue},

url = {https://aclanthology.org/2024.lrec-main.1128},

year  = {2024},

date = {2024-05-01},

urldate = {2024-05-01},

booktitle = {Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)},

pages = {12879–12892},

publisher = {ELRA and ICCL},

address = {Torino, Italy},

abstract = {Populism is a phenomenon that is noticeably present in the political landscape of various countries over the past decades. While populism expressed by politicians has been thoroughly examined in the literature, populism expressed by citizens is still underresearched, especially when it comes to its automated detection in text. This work presents the PopAut corpus, which is the first annotated corpus of news comments for populism in the German language. It features 1,200 comments collected between 2019-2021 that are annotated for populist motives anti-elitism, people-centrism and people-sovereignty. Following the definition of Cas Mudde, populism is seen as a thin ideology. This work shows that annotators reach a high agreement when labeling news comments for these motives. The data set is collected to serve as the basis for automated populism detection using machine-learning methods. By using transformer-based models, we can outperform existing dictionaries tailored for automated populism detection in German social media content. Therefore our work provides a rich resource for future work on the classification of populist user comments in the German language.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{10.1007/978-3-031-58839-6_26,

title = {Unlocking the Potential of Content-Based Restaurant Recommender Systems},

author = {Dante Godolja and Thomas Elmar Kolb and Julia Neidhardt},

editor = {Katerina Berezina and Lyndon Nixon and Aarni Tuomi},

isbn = {978-3-031-58839-6},

year  = {2024},

date = {2024-01-01},

booktitle = {Information and Communication Technologies in Tourism 2024},

pages = {239–244},

publisher = {Springer Nature Switzerland},

address = {Cham},

abstract = {Content-based restaurant recommender systems use features such as cuisine type, price range, and location to suggest dining options to users. Current research explores ways to improve their effectiveness. In this work, we explore different ideas on how to build a recommender system. We explore TF-IDF as a baseline and the state-of-the-art model SBERT. These ideas are tested on a real-world data-set of a digital restaurant guide. Evaluation involves both qualitative assessment by a domain expert and quantitative analysis. The results show that, with proper preprocessing, TF-IDF can achieve similar scores to SBERT and, depending on the scenario, even better results. However, SBERT still provides more novel recommendations than TF-IDF. Depending on the scenario, both models can be used to generate meaningful restaurant recommendations. However, more implicit aspects like a restaurant's atmosphere can hardly be captured by these models.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{10.1145/3631700.3665187,

title = {Classifying User Roles in Online News Forums: A Model for User Interaction and Behavior Analysis},

author = {Felix Scholz and Thomas Elmar Kolb and Julia Neidhardt},

url = {https://doi.org/10.1145/3631700.3665187},

doi = {10.1145/3631700.3665187},

isbn = {9798400704666},

year  = {2024},

date = {2024-01-01},

booktitle = {Adjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization},

pages = {240–249},

publisher = {Association for Computing Machinery},

address = {Cagliari, Italy},

series = {UMAP Adjunct '24},

abstract = {The growing exchange of opinions in online news forums brings together a diverse cross-section of users with varying opinions and motivations. Understanding these behaviors is crucial for unraveling the composition of these large user bases. This study proposes an explainable model aimed at classifying users based on their activity and interaction patterns in online news forums. The model leverages exploratory and statistical data analysis to reveal recurring behaviors and provides a tool to analyze the evolution of large user communities, offering an overview of their composition. The model identifies six active roles: Taciturn, Silent Voter, Regular, Conversationalist, Power User, and Celebrity, and one inactive role, Lurker. The model was evaluated for its predictive power, achieving a macro F1 score of 0.8632, demonstrating its robustness. By applying the model to a long-term dataset from the online news forum derStandard.at, an analysis of role distribution over time was conducted. The results indicated a gradual increase in user activity within the forum. Moreover, the study assessed the co-occurrence of roles in users’ long-term behavior and measured the frequency of role changes. This analysis aimed to determine whether users have consistent roles or exhibit various roles, which may depend on time or context.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@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.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{10.1145/3631700.3664897,

title = {Evaluating Group Fairness in News Recommendations: A Comparative Study of Algorithms and Metrics},

author = {Blake Huebner and Thomas Elmar Kolb and Julia Neidhardt},

url = {https://doi.org/10.1145/3631700.3664897},

doi = {10.1145/3631700.3664897},

isbn = {9798400704666},

year  = {2024},

date = {2024-01-01},

booktitle = {Adjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization},

pages = {337–346},

publisher = {Association for Computing Machinery},

address = {Cagliari, Italy},

series = {UMAP Adjunct '24},

abstract = {Beyond accuracy metrics, such as fairness and diversity, have become widely studied topics in recommender systems. Improving these metrics is important not only from an ethical and legal perspective, but can also improve overall user satisfaction. Although these metrics are widely discussed, very little empirical research has been done, especially comparing multiple algorithms across different metrics. This work explores the role of fairness and diversity in news recommender systems, specifically in the context of the Austrian media landscape. This study aims to identify the most effective approaches for generating fair and diverse news recommendations, while addressing the potential negative consequences of biased recommendations and filter bubbles, such as societal polarization and the suppression of information. This includes an extensive literature review of relevant group unfairness metrics and state-of-the-art fairness-aware algorithms. A dataset of articles from an Austrian newspaper was used for empirical research, with analysis performed on fairness, and diversity of recommendations. The key message of the study is that accuracy and fairness can be achieved simultaneously with the right modeling approach, while diversity can be held constant using these modeling techniques. The study recommends the use of Personalized Fairness based on Causal Notion models for accuracy and reducing certain unfairness metrics, and finds Fairness Objectives for Collaborative Filtering models more effective at reducing other types of unfairness. The findings contribute to the field by demonstrating the importance of incorporating these metrics into the design and evaluation of recommender systems.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{10.1145/3640457.3688027,

title = {Enhancing Cross-Domain Recommender Systems with LLMs: Evaluating Bias and Beyond-Accuracy Measures},

author = {Thomas Elmar Kolb},

url = {https://doi.org/10.1145/3640457.3688027},

doi = {10.1145/3640457.3688027},

isbn = {9798400705052},

year  = {2024},

date = {2024-01-01},

booktitle = {Proceedings of the 18th ACM Conference on Recommender Systems},

pages = {1388–1394},

publisher = {Association for Computing Machinery},

address = {Bari, Italy},

series = {RecSys '24},

abstract = {The research domain of recommender systems is rapidly evolving. Initially, optimization efforts focused primarily on accuracy. However, recent research has highlighted the importance of addressing bias and beyond-accuracy measures such as novelty, diversity, and serendipity. With the rise of multi-domain recommender systems, the need to re-examine bias and beyond-accuracy measures in cross-domain settings has become crucial. Traditional methods face challenges such as cold-start problems, which can potentially be mitigated by leveraging LLMs. This proposed work investigates how LLM-based recommendation methods can enhance cross-domain recommender systems, focusing on identifying, measuring, and mitigating bias while evaluating the impact of beyond-accuracy measures. We aim to provide new insights by comparing traditional and LLM-based systems within a real-world environment encompassing the domains of news, books, and various lifestyle areas. Our research seeks to address the outlined gaps and develop effective evaluation strategies for the unique challenges posed by LLMs in cross-domain recommender systems.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@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.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{	20.500.12708_191183,

title = {Potentials of Combining Local Knowledge and LLMs for Recommender Systems},

author = {Thomas Elmar Kolb and Ahmadou Wagne and Mete Sertkan and Julia Neidhardt},

editor = {Vito Walter Anelli and Pierpaolo Basile and Gerard De Melo and Francesco Donini and Antonio Ferrara and Cataldo Musto and Fedelucio Narducci and Azzurra Ragone and Markus Zanker},

doi = {10.34726/5334},

year  = {2023},

date = {2023-01-01},

volume = {3560},

pages = {61–64},

publisher = {CEUR-WS.org},

series = {CEUR Workshop Proceedings},

abstract = {LLMs have revolutionized the understanding and generation of natural language, offering new possibilities for enhancing recommendation systems. In previous studies, LLMs exploit their global knowledge to provide zero- or few-shot recommendations. In this work, we aim to highlight the opportunities that LLMs pose to enrich the field of recommender systems combined with local knowledge. We propose to view recommender systems combined with LLMs from a broader perspective, recognizing them not merely as another method to replace existing recommendation approaches, but rather as a complementary and powerful approach to enhance and augment the overall recommendation process.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{	20.500.12708_177094,

title = {The ALPIN Sentiment Dictionary: Austrian Language Polarity in Newspapers},

author = {Thomas Kolb and Katharina Sekanina and Bettina Manuela Johanna Kern and Julia Neidhardt and Tanja Wissik and Andreas Baumann},

doi = {10.34726/4101},

year  = {2022},

date = {2022-01-01},

pages = {4708–4716},

publisher = {European Language Resources Association},

address = {Marseille, France},

abstract = {This paper introduces the Austrian German sentiment dictionary ALPIN to account for the lack of resources for dictionary-based sentiment analysis in this specific variety of German, which is characterized by lexical idiosyncrasies that also affect word sentiment. The proposed language resource is based on Austrian news media in the field of politics, an austriacism list based on different resources and a posting data set based on a popular Austrian news media. Different resources are used to increase the diversity of the resulting language resource. Extensive crowd-sourcing is performed followed by evaluation and automatic conversion into sentiment scores. We show that crowd-sourcing enables the creation of a sentiment dictionary for the Austrian German domain. Additionally, the different parts of the sentiment dictionary are evaluated to show their impact on the resulting resource. Furthermore, the proposed dictionary is utilized in a web application and available for future research and free to use for anyone.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@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.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{	20.500.12708_55666,

title = {A review and cluster analysis of German polarity resources for sentiment analysis},

author = {Bettina Kern and Andreas Baumann and Thomas Kolb and Katharina Sekanina and Klaus Hofmann and Tanja Wissik and Julia Neidhardt},

doi = {10.4230/OASIcs.LDK.2021.37},

isbn = {978-3-95977-199-3},

year  = {2021},

date = {2021-01-01},

booktitle = {3rd Conference on Language, Data and Knowledge (LDK 2021)},

pages = {1–17},

publisher = {OASICS},

address = {93},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}