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Publications from 2020 and before are not direct results of the SFI MediaFutures, but are key results from our team members working on related topics in MediaFutures.
2026
Bahareh Fatemi; Fazle Rabbi; Yngve Lamo; Andreas L. Opdahl
A Framework for Comparative Analysis of News Content: A Model-Based Approach Journal Article Forthcoming
In: Communications in Computer and Information Science, Springer, Forthcoming.
Abstract | BibTeX | Tags: | Links:
@article{framewoerk25,
title = {A Framework for Comparative Analysis of News Content: A Model-Based Approach},
author = {Bahareh Fatemi and Fazle Rabbi and Yngve Lamo and Andreas L. Opdahl},
url = {https://mediafutures.no/ccis/},
year = {2026},
date = {2026-08-02},
urldate = {2025-08-02},
journal = {Communications in Computer and Information Science, Springer},
abstract = {In the digital age, the volume of news data available from diverse
sources is vast and continually growing. On the one hand, the quantity of information can overwhelm reporters and on the other hand, news reporting is further complicated by the inherent complexities of multifaceted events that evolve over time, as well as the biases and perspectives that different reporters and media outlets bring to their coverage. Despite such challenges, journalists must report on events in a timely and ethical manner. However, there is a lack of computational methods for analyzing massive news streams in an explainable and responsible way. In this paper, we propose a content based news analysis framework based on news comparison that enables modeling various analytical tasks such as analyzing the perspectives of news publishers, monitoring the progression of news events from various perspectives, exploring the evolution patterns of events over time and analyzing news article variants and for uncovering underlying story-lines. Our approach utilizes a knowledge graph to represent key concepts in the news domain, such as events and their contextual information, across various dimensions. This facilitates a multi-dimensional and comparative analysis of news article variants. We demonstrate the practical applicability of our method through a running example. By adopting a model-based approach, our framework offers the flexibility needed to represent a broad spectrum of domain concepts.},
keywords = {},
pubstate = {forthcoming},
tppubtype = {article}
}
In the digital age, the volume of news data available from diverse
sources is vast and continually growing. On the one hand, the quantity of information can overwhelm reporters and on the other hand, news reporting is further complicated by the inherent complexities of multifaceted events that evolve over time, as well as the biases and perspectives that different reporters and media outlets bring to their coverage. Despite such challenges, journalists must report on events in a timely and ethical manner. However, there is a lack of computational methods for analyzing massive news streams in an explainable and responsible way. In this paper, we propose a content based news analysis framework based on news comparison that enables modeling various analytical tasks such as analyzing the perspectives of news publishers, monitoring the progression of news events from various perspectives, exploring the evolution patterns of events over time and analyzing news article variants and for uncovering underlying story-lines. Our approach utilizes a knowledge graph to represent key concepts in the news domain, such as events and their contextual information, across various dimensions. This facilitates a multi-dimensional and comparative analysis of news article variants. We demonstrate the practical applicability of our method through a running example. By adopting a model-based approach, our framework offers the flexibility needed to represent a broad spectrum of domain concepts.
sources is vast and continually growing. On the one hand, the quantity of information can overwhelm reporters and on the other hand, news reporting is further complicated by the inherent complexities of multifaceted events that evolve over time, as well as the biases and perspectives that different reporters and media outlets bring to their coverage. Despite such challenges, journalists must report on events in a timely and ethical manner. However, there is a lack of computational methods for analyzing massive news streams in an explainable and responsible way. In this paper, we propose a content based news analysis framework based on news comparison that enables modeling various analytical tasks such as analyzing the perspectives of news publishers, monitoring the progression of news events from various perspectives, exploring the evolution patterns of events over time and analyzing news article variants and for uncovering underlying story-lines. Our approach utilizes a knowledge graph to represent key concepts in the news domain, such as events and their contextual information, across various dimensions. This facilitates a multi-dimensional and comparative analysis of news article variants. We demonstrate the practical applicability of our method through a running example. By adopting a model-based approach, our framework offers the flexibility needed to represent a broad spectrum of domain concepts.
Ingar M Arntzen; Njål Borch; Anders Andersen
State-based Layering. A conceptual framework for live production of time-driven media experiences on data-driven consumer platforms Conference
14’th Computing Conference, London UK, 2026.
Abstract | BibTeX | Tags: | Links:
@conference{nokey,
title = {State-based Layering. A conceptual framework for live production of time-driven media experiences on data-driven consumer platforms},
author = {Ingar M Arntzen and Njål Borch and Anders Andersen},
url = {https://mediafutures.no/final_arntzen_sbl_cc2026/},
year = {2026},
date = {2026-07-10},
urldate = {2026-07-01},
booktitle = {14’th Computing Conference, London UK},
abstract = {Web-based technologies are widely used as production tools for time-driven media and as interfaces for live reporting of time-sensitive developments. Time-driven media face growing demands for interactivity, adaptation, and personalization, suggesting a more web-like model where layer orchestration and time-sensitive rendering increasingly occur on the client side, within data-driven consumer interfaces. Yet realizing this shift is challenging. Time-driven media production relies on precise control over layering—when layers are activated, deactivated, and how they are combined and displayed. In data-driven platforms, however, support for external, time-sensitive control is weak, and layer orchestration and playback are handled by distinct media frameworks—each constrained to a particular application context and technology stack. Our approach is to shift the problem from media frameworks to the data model. By supporting production-controlled layering and playback within application state, time-driven media experiences can be realized as native expressions of data-driven platforms, fully exploiting available rendering technologies and backend services. The main contribution is State-based Layering (SbL), a conceptual framework for developing controllable, time-dependent application state. SbL generalizes concepts defined within existing media frameworks, promoting them as generic programming constructs—independent of data formats and user interfaces, and applicable across diverse application contexts. StateLayers, a reference implementation of SbL, shows that timing, control, and layering can be fully encapsulated within application state and remain compatible with modern development workflows. SbL marks an important step toward scalable personalization, enabling production systems to directly shape unique time-driven narratives for individual users through real-time control over consumer interfaces.},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
Web-based technologies are widely used as production tools for time-driven media and as interfaces for live reporting of time-sensitive developments. Time-driven media face growing demands for interactivity, adaptation, and personalization, suggesting a more web-like model where layer orchestration and time-sensitive rendering increasingly occur on the client side, within data-driven consumer interfaces. Yet realizing this shift is challenging. Time-driven media production relies on precise control over layering—when layers are activated, deactivated, and how they are combined and displayed. In data-driven platforms, however, support for external, time-sensitive control is weak, and layer orchestration and playback are handled by distinct media frameworks—each constrained to a particular application context and technology stack. Our approach is to shift the problem from media frameworks to the data model. By supporting production-controlled layering and playback within application state, time-driven media experiences can be realized as native expressions of data-driven platforms, fully exploiting available rendering technologies and backend services. The main contribution is State-based Layering (SbL), a conceptual framework for developing controllable, time-dependent application state. SbL generalizes concepts defined within existing media frameworks, promoting them as generic programming constructs—independent of data formats and user interfaces, and applicable across diverse application contexts. StateLayers, a reference implementation of SbL, shows that timing, control, and layering can be fully encapsulated within application state and remain compatible with modern development workflows. SbL marks an important step toward scalable personalization, enabling production systems to directly shape unique time-driven narratives for individual users through real-time control over consumer interfaces.
Christoph Trattner; Svenja Lys Forstner; Alain D. Starke; Erik Knudsen
C2PA Provenance Labels Increase Trust in News Platforms Across Western Countries Conference Forthcoming
AAAI ICWSM 2026, Forthcoming.
Abstract | BibTeX | Tags: WP2: User Modeling Personalization and Engagement | Links:
@conference{nokey,
title = {C2PA Provenance Labels Increase Trust in News Platforms Across Western Countries},
author = {Christoph Trattner and Svenja Lys Forstner and Alain D. Starke and Erik Knudsen},
url = {https://mediafutures.no/c2pa_icwsm_2026/},
year = {2026},
date = {2026-05-05},
urldate = {2026-05-05},
booktitle = {AAAI ICWSM 2026},
abstract = {Misinformation and disinformation threaten global public trust in news media. Generative AI exacerbates mistrust by making it difficult to distinguish authentic images from AI-generated ones. This study examines whether accompanying images with C2PA (Coalition for Content Provenance and Authenticity) provenance labels can restore trust. C2PA is an open standard that cryptographically secures and describes a media file’s origin and editing history. We conducted an online experiment with $N=6,114$ participants, reflecting audiences of six major news sources in the US, UK, and Norway. Each participant evaluated six news article previews with images, either accompanied by a provenance label (three levels of detail) or not. Presenting provenance metadata to participants significantly improved their perceptions of an image’s transparency and credibility, and also increased feelings of trust in a presented news source. These results show that verifiable provenance makes visual content more inspectable and strengthens brand trust. By adopting C2PA or similar frameworks, news organizations can counter AI-generated disinformation and improve audience trust.},
keywords = {WP2: User Modeling Personalization and Engagement},
pubstate = {forthcoming},
tppubtype = {conference}
}
Misinformation and disinformation threaten global public trust in news media. Generative AI exacerbates mistrust by making it difficult to distinguish authentic images from AI-generated ones. This study examines whether accompanying images with C2PA (Coalition for Content Provenance and Authenticity) provenance labels can restore trust. C2PA is an open standard that cryptographically secures and describes a media file’s origin and editing history. We conducted an online experiment with $N=6,114$ participants, reflecting audiences of six major news sources in the US, UK, and Norway. Each participant evaluated six news article previews with images, either accompanied by a provenance label (three levels of detail) or not. Presenting provenance metadata to participants significantly improved their perceptions of an image’s transparency and credibility, and also increased feelings of trust in a presented news source. These results show that verifiable provenance makes visual content more inspectable and strengthens brand trust. By adopting C2PA or similar frameworks, news organizations can counter AI-generated disinformation and improve audience trust.
2025
Peter Røysland Aarnes; Vinay Jayarama Setty
NumPert: Numerical Perturbations to Probe Language Models for Veracity Prediction Conference
2025.
BibTeX | Tags: WP3: Media Content Production and Analysis | Links:
@conference{nokey,
title = {NumPert: Numerical Perturbations to Probe Language Models for Veracity Prediction},
author = {Peter Røysland Aarnes and Vinay Jayarama Setty},
url = {https://mediafutures.no/numpert_numerical_perturbations_to_probe_language_models_for_veracity_prediction/},
year = {2025},
date = {2025-12-20},
journal = {International Joint Conference on Natural Language Processing & Asia-Pacific Chapter of the Association for Computational Linguistics Student Research Workshop},
keywords = {WP3: Media Content Production and Analysis},
pubstate = {published},
tppubtype = {conference}
}
Peter Røysland Aarnes
Explainable Numerical Claim Verification Conference
ACM International Conference on Information and Knowledge Management (CIKM '25)), 2025.
Abstract | BibTeX | Tags: WP3: Media Content Production and Analysis | Links:
@conference{nokey,
title = {Explainable Numerical Claim Verification},
author = {Peter Røysland Aarnes},
url = {https://mediafutures.no/explainable-numerical-claim-verification/},
year = {2025},
date = {2025-11-10},
urldate = {2025-11-10},
booktitle = {ACM International Conference on Information and Knowledge Management (CIKM '25))},
abstract = {The rapid proliferation of mis- and disinformation in the digital age highlights the urgent need for scalable, transparent, and trustworthy automated fact-checking systems. Large Language Models (LLMs) offer strong language understanding capabilities but suffer from opacity and brittleness, particularly in reasoning over numerical claims. This work explores how Explainable Artificial Intelligence (XAI)-through the lens of counterfactual explanations and adversarial training-can be used to systematically evaluate and improve the robustness of LLMs against perturbed numerical inputs. We propose a framework that employs counterfactual generation to both probe LLM reliability and generate user-appropriate explanations. Through empirical evaluations using a large-scale numerical fact-checking dataset (QuanTemp), we show that even state-of-the-art LLMs are susceptible to subtle numerical perturbations, impacting verdict accuracy. Our methodology contributes a dual-purpose diagnostic and training strategy that not only bolsters robustness but also enables both global and local interpretability-thereby improving explainability in automated fact-checking systems.},
keywords = {WP3: Media Content Production and Analysis},
pubstate = {published},
tppubtype = {conference}
}
The rapid proliferation of mis- and disinformation in the digital age highlights the urgent need for scalable, transparent, and trustworthy automated fact-checking systems. Large Language Models (LLMs) offer strong language understanding capabilities but suffer from opacity and brittleness, particularly in reasoning over numerical claims. This work explores how Explainable Artificial Intelligence (XAI)-through the lens of counterfactual explanations and adversarial training-can be used to systematically evaluate and improve the robustness of LLMs against perturbed numerical inputs. We propose a framework that employs counterfactual generation to both probe LLM reliability and generate user-appropriate explanations. Through empirical evaluations using a large-scale numerical fact-checking dataset (QuanTemp), we show that even state-of-the-art LLMs are susceptible to subtle numerical perturbations, impacting verdict accuracy. Our methodology contributes a dual-purpose diagnostic and training strategy that not only bolsters robustness but also enables both global and local interpretability-thereby improving explainability in automated fact-checking systems.
Adane Nega Tarekegn; Fazle Rabbi; Lubos Steskal; Bjørnar Tessem
Automated News Clip Generation via Robust Video Summarization Conference
37th International Conference on Tools with Artificial Intelligence (CITAI2025), 2025.
BibTeX | Tags:
@conference{automatednews25,
title = {Automated News Clip Generation via Robust Video Summarization},
author = {Adane Nega Tarekegn and Fazle Rabbi and Lubos Steskal and Bjørnar Tessem},
year = {2025},
date = {2025-11-05},
urldate = {2025-11-05},
booktitle = {37th International Conference on Tools with Artificial Intelligence (CITAI2025)},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
Gloria Anne Babile Kasangu; Alain D. Starke; Christoph Trattner
More of the Same? A Longitudinal Evaluation of Two Similarity-based Approaches in a News Recommender System Proceedings Article
In: Proceedings of the 13th International Workshop on News Recommendation and Analytics (INRA 2025), 2025.
Abstract | BibTeX | Tags: | Links:
@inproceedings{morefothesame,
title = {More of the Same? A Longitudinal Evaluation of Two Similarity-based Approaches in a News Recommender System},
author = {Gloria Anne Babile Kasangu and Alain D. Starke and Christoph Trattner},
url = {https://mediafutures.no/recsys2025_inra_longitudinal_news___ceur/},
year = {2025},
date = {2025-10-01},
booktitle = {Proceedings of the 13th International Workshop on News Recommendation and Analytics (INRA 2025)},
abstract = {Similarity-based personalization is generally assumed to boost engagement in recommender systems. However, is
this also true beyond a single session in a news recommender? Amid concerns about filter bubbles and preference volatility, we propose an empirical evaluation of both short-term and longer-term effects of a news recommender system with two phases of data collection: Initial preference elicitation and evaluation (Phase 1), a 48-hour interval, and a personalized follow-up (Phase 2). We compared two recommendation strategies in a preliminary longitudinal experiment (? = 166): An ‘Aligned’ feed that included articles that met a ≥ 70% cosine‐similarity threshold, and a ‘Disaligned’ feed with only a 30% similarity threshold. We collected behavioral metrics (article clicks, time on feed) and evaluative metrics (self-reported familiarity, perceived recommendation quality, choice satisfaction, topic preferences) in both phases. The Aligned feed was perceived to have more familiar content, while perceived diversity did not differ between recommendation strategies. Users clicked on significantly fewer articles in Phase 2, particularly in the Disaligned condition. We also explored the volatility of topic preferences, but did not observe significant differences across phases. These findings suggest that short-term increases in feed–profile similarity can enhance familiarity and maintain behavioral engagement (i.e., clicks). In contrast, they do not lead to higher levels of perceived quality and choice satisfaction, which raises doubts about the relationship between the similarity of preference-based articles and user satisfaction.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Similarity-based personalization is generally assumed to boost engagement in recommender systems. However, is
this also true beyond a single session in a news recommender? Amid concerns about filter bubbles and preference volatility, we propose an empirical evaluation of both short-term and longer-term effects of a news recommender system with two phases of data collection: Initial preference elicitation and evaluation (Phase 1), a 48-hour interval, and a personalized follow-up (Phase 2). We compared two recommendation strategies in a preliminary longitudinal experiment (? = 166): An ‘Aligned’ feed that included articles that met a ≥ 70% cosine‐similarity threshold, and a ‘Disaligned’ feed with only a 30% similarity threshold. We collected behavioral metrics (article clicks, time on feed) and evaluative metrics (self-reported familiarity, perceived recommendation quality, choice satisfaction, topic preferences) in both phases. The Aligned feed was perceived to have more familiar content, while perceived diversity did not differ between recommendation strategies. Users clicked on significantly fewer articles in Phase 2, particularly in the Disaligned condition. We also explored the volatility of topic preferences, but did not observe significant differences across phases. These findings suggest that short-term increases in feed–profile similarity can enhance familiarity and maintain behavioral engagement (i.e., clicks). In contrast, they do not lead to higher levels of perceived quality and choice satisfaction, which raises doubts about the relationship between the similarity of preference-based articles and user satisfaction.
this also true beyond a single session in a news recommender? Amid concerns about filter bubbles and preference volatility, we propose an empirical evaluation of both short-term and longer-term effects of a news recommender system with two phases of data collection: Initial preference elicitation and evaluation (Phase 1), a 48-hour interval, and a personalized follow-up (Phase 2). We compared two recommendation strategies in a preliminary longitudinal experiment (? = 166): An ‘Aligned’ feed that included articles that met a ≥ 70% cosine‐similarity threshold, and a ‘Disaligned’ feed with only a 30% similarity threshold. We collected behavioral metrics (article clicks, time on feed) and evaluative metrics (self-reported familiarity, perceived recommendation quality, choice satisfaction, topic preferences) in both phases. The Aligned feed was perceived to have more familiar content, while perceived diversity did not differ between recommendation strategies. Users clicked on significantly fewer articles in Phase 2, particularly in the Disaligned condition. We also explored the volatility of topic preferences, but did not observe significant differences across phases. These findings suggest that short-term increases in feed–profile similarity can enhance familiarity and maintain behavioral engagement (i.e., clicks). In contrast, they do not lead to higher levels of perceived quality and choice satisfaction, which raises doubts about the relationship between the similarity of preference-based articles and user satisfaction.
Tobias J. Wessel; Christoph Trattner; Alain D. Starke
Using Large Language Models to ‘Lighten the Mood’: Satirically Reframing News Recommendations to Reduce News Avoidance Proceedings
2025.
Abstract | BibTeX | Tags: WP2: User Modeling Personalization and Engagement | Links:
@proceedings{usinglarge25,
title = {Using Large Language Models to ‘Lighten the Mood’: Satirically Reframing News Recommendations to Reduce News Avoidance},
author = {Tobias J. Wessel and Christoph Trattner and Alain D. Starke},
url = {https://mediafutures.no/fullpaper_wessel_et_al_inra2025/},
year = {2025},
date = {2025-09-26},
issue = {INRA 2025/RecSys25},
abstract = {News avoidance is a growing issue that leads to less informed citizens and endangers democratic processes. This
also poses problems in news recommender environments, as ’unpleasant’ news content could be avoided through
personalized algorithms. To ‘lighten the user’s mood’, this paper investigates whether satirical re-framing of
news article summaries, generated by Large Language Models (LLMs), can mitigate news avoidance by making
news content more engaging. Through two online experiments (? = 89; ? = 151), we tested various prompting
techniques, assessing the impact on user perception, humor, understanding, and news consumption choices.
Results indicate that satirically re-framed summaries were perceived to be engaging and informative. Less
frequent news consumers showed a stronger preference for satirical content, suggesting that satire could be a
tool for reconnecting with disengaged audiences. These findings show the promise of AI-generated personalized
satire as an innovative approach to reducing news avoidance.},
keywords = {WP2: User Modeling Personalization and Engagement},
pubstate = {published},
tppubtype = {proceedings}
}
News avoidance is a growing issue that leads to less informed citizens and endangers democratic processes. This
also poses problems in news recommender environments, as ’unpleasant’ news content could be avoided through
personalized algorithms. To ‘lighten the user’s mood’, this paper investigates whether satirical re-framing of
news article summaries, generated by Large Language Models (LLMs), can mitigate news avoidance by making
news content more engaging. Through two online experiments (? = 89; ? = 151), we tested various prompting
techniques, assessing the impact on user perception, humor, understanding, and news consumption choices.
Results indicate that satirically re-framed summaries were perceived to be engaging and informative. Less
frequent news consumers showed a stronger preference for satirical content, suggesting that satire could be a
tool for reconnecting with disengaged audiences. These findings show the promise of AI-generated personalized
satire as an innovative approach to reducing news avoidance.
also poses problems in news recommender environments, as ’unpleasant’ news content could be avoided through
personalized algorithms. To ‘lighten the user’s mood’, this paper investigates whether satirical re-framing of
news article summaries, generated by Large Language Models (LLMs), can mitigate news avoidance by making
news content more engaging. Through two online experiments (? = 89; ? = 151), we tested various prompting
techniques, assessing the impact on user perception, humor, understanding, and news consumption choices.
Results indicate that satirically re-framed summaries were perceived to be engaging and informative. Less
frequent news consumers showed a stronger preference for satirical content, suggesting that satire could be a
tool for reconnecting with disengaged audiences. These findings show the promise of AI-generated personalized
satire as an innovative approach to reducing news avoidance.
Svenja Lys Forstner; Yelyzaveta Lysova; Alain D. Starke; Christoph Trattner
Evaluating Image Trust Labels in a News Recommender System Proceedings
2025.
Abstract | BibTeX | Tags: WP2: User Modeling Personalization and Engagement | Links:
@proceedings{evalu25,
title = {Evaluating Image Trust Labels in a News Recommender System},
author = {Svenja Lys Forstner and Yelyzaveta Lysova and Alain D. Starke and Christoph Trattner},
url = {https://mediafutures.no/ceur___inra_2025_short_workshop_paper-3/},
year = {2025},
date = {2025-09-26},
urldate = {2025-09-26},
issue = {INRA 2025/RecSys25},
abstract = {Rising user concerns about online misinformation and the spread of AI-generated visual content underscore the need for better ways to verify image authenticity. Image provenance labels are a proposed solution, aiming to help users assess the veracity of digital images. The Coalition for Content Provenance and Authenticity (C2PA), for instance, can disclose image provenance (i.e., origin or source details) to users in the form of labels that describe the image's metadata. However, little is known about whether users engage with or understand such labels, especially in news recommender contexts. In this paper, we introduce an alternative `Image Trust Score' label, inspired by the front-of-package Nutri-Score label, and experimentally evaluate its effectiveness in a personalized news setting. We present the results of a four-condition (no-label baseline, C2PA label, black-and-white and colored Image Trust Score) between-subjects study (N=202) in which participants selected news articles (with or without labels), reporting on label comprehension and trust. While image trust and article selection were not significantly affected, all labels increased article trust. The Image Trust Score was perceived as more understandable and appealing than the C2PA label, though many participants misinterpreted the labels' meaning. Our findings highlight the need for clearer and more intuitive provenance label design.},
keywords = {WP2: User Modeling Personalization and Engagement},
pubstate = {published},
tppubtype = {proceedings}
}
Rising user concerns about online misinformation and the spread of AI-generated visual content underscore the need for better ways to verify image authenticity. Image provenance labels are a proposed solution, aiming to help users assess the veracity of digital images. The Coalition for Content Provenance and Authenticity (C2PA), for instance, can disclose image provenance (i.e., origin or source details) to users in the form of labels that describe the image's metadata. However, little is known about whether users engage with or understand such labels, especially in news recommender contexts. In this paper, we introduce an alternative `Image Trust Score' label, inspired by the front-of-package Nutri-Score label, and experimentally evaluate its effectiveness in a personalized news setting. We present the results of a four-condition (no-label baseline, C2PA label, black-and-white and colored Image Trust Score) between-subjects study (N=202) in which participants selected news articles (with or without labels), reporting on label comprehension and trust. While image trust and article selection were not significantly affected, all labels increased article trust. The Image Trust Score was perceived as more understandable and appealing than the C2PA label, though many participants misinterpreted the labels' meaning. Our findings highlight the need for clearer and more intuitive provenance label design.
Jørgen Eknes-Riple; Jia Hua Jeng; Alain D. Starke; Khadiga Seddik; Christoph Trattner
Hope, Fear, or Anger? How Emotional Framing in a News Recommender System Guides User Preferences Working paper
2025.
Abstract | BibTeX | Tags: WP2: User Modeling Personalization and Engagement | Links:
@workingpaper{hopefear25,
title = {Hope, Fear, or Anger? How Emotional Framing in a News Recommender System Guides User Preferences},
author = {Jørgen Eknes-Riple and Jia Hua Jeng and Alain D. Starke and Khadiga Seddik and Christoph Trattner},
url = {https://mediafutures.no/recsys_inra_2025/},
year = {2025},
date = {2025-09-26},
urldate = {2025-09-26},
issue = {RecSys2025 - INRA workshop},
abstract = {News recommender systems (NRSs) increasingly leverage artificial intelligence to automate journalistic processes and tailor content to individual users. These systems are shaping patterns of news consumption. The emotional reframing of the content of the news article, applied through large language models (LLM), has the potential to influence the selection of the articles of users and guide them towards specific content. This paper explores how emotional reframing of news articles can influence user engagement, interaction, and openness to non-preferred content. We present the results of a user study (N = 150) on a news platform. How news articles were presented was subject to a 3x2-mixed research design. News articles were rewritten using a large language model (LLM) in one of three emotional tones: fearful, angry, or hopeful. Moreover, articles either aligned with the user's emotional state and topical preferences or not. These emotionally reframed articles were then either aligned or misaligned with users' self-reported emotional state to examine the effect of emotional alignment. The results show that emotional alignment significantly increased the likelihood that users selected an article as their favorite, even when it belonged to their least preferred topic category. This finding suggests that emotional alignment can guide users toward content they might otherwise avoid, offering a potential means to reduce selective exposure. In terms of behavioral engagement, articles reframed with an angry tone significantly led to longer reading times, while fearfully framed articles were more likely to be clicked. In contrast, hopeful framing resulted in reduced interaction, which suggests that negative rather than positive emotions increase user engagement.},
keywords = {WP2: User Modeling Personalization and Engagement},
pubstate = {published},
tppubtype = {workingpaper}
}
News recommender systems (NRSs) increasingly leverage artificial intelligence to automate journalistic processes and tailor content to individual users. These systems are shaping patterns of news consumption. The emotional reframing of the content of the news article, applied through large language models (LLM), has the potential to influence the selection of the articles of users and guide them towards specific content. This paper explores how emotional reframing of news articles can influence user engagement, interaction, and openness to non-preferred content. We present the results of a user study (N = 150) on a news platform. How news articles were presented was subject to a 3x2-mixed research design. News articles were rewritten using a large language model (LLM) in one of three emotional tones: fearful, angry, or hopeful. Moreover, articles either aligned with the user's emotional state and topical preferences or not. These emotionally reframed articles were then either aligned or misaligned with users' self-reported emotional state to examine the effect of emotional alignment. The results show that emotional alignment significantly increased the likelihood that users selected an article as their favorite, even when it belonged to their least preferred topic category. This finding suggests that emotional alignment can guide users toward content they might otherwise avoid, offering a potential means to reduce selective exposure. In terms of behavioral engagement, articles reframed with an angry tone significantly led to longer reading times, while fearfully framed articles were more likely to be clicked. In contrast, hopeful framing resulted in reduced interaction, which suggests that negative rather than positive emotions increase user engagement.
Ayoub El Majjodi; Alain D. Starke; Alessandro Petruzzelli; Cataldo Musto
Nudging Healthy Choices: Leveraging LLM-Generated Hashtags and Explanations in Personalized Food Recommendations Workshop
2025.
Abstract | BibTeX | Tags: | Links:
@workshop{nudging25,
title = {Nudging Healthy Choices: Leveraging LLM-Generated Hashtags and Explanations in Personalized Food Recommendations},
author = {Ayoub El Majjodi and Alain D. Starke and Alessandro Petruzzelli and Cataldo Musto},
url = {https://mediafutures.no/llms/},
year = {2025},
date = {2025-09-26},
urldate = {2025-09-26},
issue = {IntRS’25},
abstract = {Making healthy recipe choices can be challenging for users, requiring time and knowledge to differentiate among
various options. These choices are often generated by personalized recommender systems that account for
individual preferences. One effective approach to encouraging healthier food choices is to intervene in how
these choices are presented to users. In this paper, we explore the impact of nutritional food labels and evaluate
the effectiveness of a Large Language Model (LLM) in generating high-quality explanations and hashtags to
support users in making healthier food decisions. In an online experiment (N = 240), we designed a knowledge-
based recommender system to generate personalized recipes for each user. Recipes were annotated with one of
four intervention, a Multiple Traffic Light (MTL) nutrition label, LLM-generated explanations, LLM-generated
hashtags, or no label (baseline). Our findings indicate that the interventions significantly enhanced users’ ability
to select healthier recipes. Additionally, we examined how different system components affected the overall user
experience and how these components interacted with one another},
keywords = {},
pubstate = {published},
tppubtype = {workshop}
}
Making healthy recipe choices can be challenging for users, requiring time and knowledge to differentiate among
various options. These choices are often generated by personalized recommender systems that account for
individual preferences. One effective approach to encouraging healthier food choices is to intervene in how
these choices are presented to users. In this paper, we explore the impact of nutritional food labels and evaluate
the effectiveness of a Large Language Model (LLM) in generating high-quality explanations and hashtags to
support users in making healthier food decisions. In an online experiment (N = 240), we designed a knowledge-
based recommender system to generate personalized recipes for each user. Recipes were annotated with one of
four intervention, a Multiple Traffic Light (MTL) nutrition label, LLM-generated explanations, LLM-generated
hashtags, or no label (baseline). Our findings indicate that the interventions significantly enhanced users’ ability
to select healthier recipes. Additionally, we examined how different system components affected the overall user
experience and how these components interacted with one another
various options. These choices are often generated by personalized recommender systems that account for
individual preferences. One effective approach to encouraging healthier food choices is to intervene in how
these choices are presented to users. In this paper, we explore the impact of nutritional food labels and evaluate
the effectiveness of a Large Language Model (LLM) in generating high-quality explanations and hashtags to
support users in making healthier food decisions. In an online experiment (N = 240), we designed a knowledge-
based recommender system to generate personalized recipes for each user. Recipes were annotated with one of
four intervention, a Multiple Traffic Light (MTL) nutrition label, LLM-generated explanations, LLM-generated
hashtags, or no label (baseline). Our findings indicate that the interventions significantly enhanced users’ ability
to select healthier recipes. Additionally, we examined how different system components affected the overall user
experience and how these components interacted with one another
John Magnus Ragnhildson Dahl
Offline in the Closet, Online and Out, then Offline, Out and Proud: The Online/Offline-ness of Teenager's Queer Worldmaking Book Chapter
In: Reynolds, Rachel R.; Pajé, Dacia; Medina, Sienna; Gigante, John (Ed.): Mediating Sex, Gender, and Sexuality in the GenZ Era, Chapter 15, pp. 14, Routledge, 2025.
BibTeX | Tags: WP1: Understanding Media Experiences | Links:
@inbook{nokey,
title = {Offline in the Closet, Online and Out, then Offline, Out and Proud: The Online/Offline-ness of Teenager's Queer Worldmaking},
author = {John Magnus Ragnhildson Dahl},
editor = {Rachel R. Reynolds and Dacia Pajé and Sienna Medina and John Gigante},
url = {https://www.taylorfrancis.com/books/edit/10.4324/9781003631316/mediating-sex-gender-sexuality-genz-era-rachel-reynolds-dacia-paj%C3%A9-sienna-medina-john-gigante},
year = {2025},
date = {2025-09-19},
booktitle = {Mediating Sex, Gender, and Sexuality in the GenZ Era},
pages = {14},
publisher = {Routledge},
chapter = {15},
keywords = {WP1: Understanding Media Experiences},
pubstate = {published},
tppubtype = {inbook}
}
Jussi Kalgren; Ekaterina Artemova; Ondrej Bojar; Marie Isabel Engels; Vladislav Mikhailov; Pavel Sindelar; Erik Velldal; Lilja Øvrelid
Overview of ELOQUENT 2025: Shared Tasks for Evaluating Generative Language Model Quality Journal Article
In: Experimental IR Meets Multilinguality, Multimodality, and Interaction , pp. 224–241, 2025.
Abstract | BibTeX | Tags: WP5: Norwegian Language Technologies | Links:
@article{nokey,
title = {Overview of ELOQUENT 2025: Shared Tasks for Evaluating Generative Language Model Quality},
author = {Jussi Kalgren and Ekaterina Artemova and Ondrej Bojar and Marie Isabel Engels and Vladislav Mikhailov and Pavel Sindelar and Erik Velldal and Lilja Øvrelid},
url = {https://link.springer.com/chapter/10.1007/978-3-032-04354-2_14},
year = {2025},
date = {2025-09-03},
journal = {Experimental IR Meets Multilinguality, Multimodality, and Interaction },
pages = { 224–241},
abstract = {ELOQUENT is a CLEF lab for evaluating generative language model quality with a focus on such aspects of quality that do not come to the fore with current standard test suites and test collections and to develop and promote new test regimes and methods that fit a multilingual application scenario for generative artificial intelligence.
This year is the second year of ELOQUENT. This year’s experiment tracks have evolved from the first year: this year we continue challenging the capability of classifiers to distinguish machine-generated from human-authored text; we explore how consistent language models are in responding to value-oriented questions across languages and system settings; we test how accurately language models are able to predict human preferences between variants of generated material; and we investigate how well language models are able provide sensible topical quizzes to fit given target texts.},
keywords = {WP5: Norwegian Language Technologies},
pubstate = {published},
tppubtype = {article}
}
ELOQUENT is a CLEF lab for evaluating generative language model quality with a focus on such aspects of quality that do not come to the fore with current standard test suites and test collections and to develop and promote new test regimes and methods that fit a multilingual application scenario for generative artificial intelligence.
This year is the second year of ELOQUENT. This year’s experiment tracks have evolved from the first year: this year we continue challenging the capability of classifiers to distinguish machine-generated from human-authored text; we explore how consistent language models are in responding to value-oriented questions across languages and system settings; we test how accurately language models are able to predict human preferences between variants of generated material; and we investigate how well language models are able provide sensible topical quizzes to fit given target texts.
This year is the second year of ELOQUENT. This year’s experiment tracks have evolved from the first year: this year we continue challenging the capability of classifiers to distinguish machine-generated from human-authored text; we explore how consistent language models are in responding to value-oriented questions across languages and system settings; we test how accurately language models are able to predict human preferences between variants of generated material; and we investigate how well language models are able provide sensible topical quizzes to fit given target texts.
Sohail Ahmed Khan; Laurence Dierickx; Jan-Gunnar Furuly; Henrik Brattli Vold; Rano Tahseen; Carl-Gustav Linden; Duc-Tien Dang-Nguyen
Debunking War Information Disorder: A Case Study in Assessing the Use of Multimedia Verification Tools Journal Article
In: Journal of the Association for Information Science and Technology, 2025.
Abstract | BibTeX | Tags: WP3: Media Content Production and Analysis | Links:
@article{warinfo24,
title = {Debunking War Information Disorder: A Case Study in Assessing the Use of Multimedia Verification Tools},
author = {Sohail Ahmed Khan and Laurence Dierickx and Jan-Gunnar Furuly and Henrik Brattli Vold and Rano Tahseen and Carl-Gustav Linden and Duc-Tien Dang-Nguyen},
url = {https://mediafutures.no/jasist_verifyingwarconflict-compressed/},
year = {2025},
date = {2025-08-13},
urldate = {2024-11-11},
journal = {Journal of the Association for Information Science and Technology},
abstract = {This paper investigates the use of multimedia verification, in particular, computational tools and Open-source Intelligence (OSINT) methods, for verifying online multimedia content in the context of the ongoing wars in Ukraine and Gaza. Our study examines the workflows and tools used by several fact-checkers and journalists working at Faktisk, a Norwegian fact-checking organisation. Our study showcases the effectiveness of diverse resources, including AI tools, geolocation tools, internet archives, and social media monitoring platforms, in enabling journalists and fact-checkers to efficiently process and corroborate evidence, ensuring the dissemination of accurate information. This research provides an in-depth analysis of the role of computational tools and OSINT methods for multimedia verification. It also underscores the potentials of currently available technology, and highlights its limitations while providing guidance for future development of digital multimedia verification tools and frameworks.},
keywords = {WP3: Media Content Production and Analysis},
pubstate = {published},
tppubtype = {article}
}
This paper investigates the use of multimedia verification, in particular, computational tools and Open-source Intelligence (OSINT) methods, for verifying online multimedia content in the context of the ongoing wars in Ukraine and Gaza. Our study examines the workflows and tools used by several fact-checkers and journalists working at Faktisk, a Norwegian fact-checking organisation. Our study showcases the effectiveness of diverse resources, including AI tools, geolocation tools, internet archives, and social media monitoring platforms, in enabling journalists and fact-checkers to efficiently process and corroborate evidence, ensuring the dissemination of accurate information. This research provides an in-depth analysis of the role of computational tools and OSINT methods for multimedia verification. It also underscores the potentials of currently available technology, and highlights its limitations while providing guidance for future development of digital multimedia verification tools and frameworks.
John Magnus Ragnhildson Dahl
Teen Boys and their Smartphones as Worldmaking Devices: In the Palm of their Hands Book
Palgrave MacMillan, 2025.
BibTeX | Tags: WP1: Understanding Media Experiences
@book{nokey,
title = {Teen Boys and their Smartphones as Worldmaking Devices: In the Palm of their Hands},
author = {John Magnus Ragnhildson Dahl},
year = {2025},
date = {2025-07-16},
urldate = {2025-07-16},
publisher = {Palgrave MacMillan},
keywords = {WP1: Understanding Media Experiences},
pubstate = {published},
tppubtype = {book}
}
Ayoub El Majjodi; Alain D. Starke; Christoph Trattner
Integrating Digital Food Nudges and Recommender Systems: Current Status and Future Directions Journal Article
In: IEEE Access, 2025.
Abstract | BibTeX | Tags: WP2: User Modeling Personalization and Engagement | Links:
@article{integratingdigital25,
title = {Integrating Digital Food Nudges and Recommender Systems: Current Status and Future Directions},
author = {Ayoub El Majjodi and Alain D. Starke and Christoph Trattner},
url = {https://mediafutures.no/integrating_digital_food_nudges_and_recommender_systems_current_status_and_future_directions/},
year = {2025},
date = {2025-07-14},
journal = {IEEE Access},
abstract = {Recommender systems are widely regarded as effective tools for facilitating the discovery of relevant content. In the food domain, they help users find recipes, choose grocery products, and generate meal suggestions. While they address the challenge of choice overload, their direct influence on promoting healthier food choices remains limited. Digital nudges could further assist in guiding users toward healthier decisions, enhancing the accessibility and visibility of healthy options when integrated into a recommender system. This review examines to what extent food recommender systems have so far successfully incorporated digital nudges for healthy food promotion and which challenges still remain. We present a classification and analysis of various digital nudging strategies employed for this purpose, as well as opportunities for future research. We emphasize that various nudging techniques have the potential to support users in making healthier food choices within food recommender systems. Furthermore, user-centric evaluations represent the most effective approach for assessing the performance of these systems.},
keywords = {WP2: User Modeling Personalization and Engagement},
pubstate = {published},
tppubtype = {article}
}
Recommender systems are widely regarded as effective tools for facilitating the discovery of relevant content. In the food domain, they help users find recipes, choose grocery products, and generate meal suggestions. While they address the challenge of choice overload, their direct influence on promoting healthier food choices remains limited. Digital nudges could further assist in guiding users toward healthier decisions, enhancing the accessibility and visibility of healthy options when integrated into a recommender system. This review examines to what extent food recommender systems have so far successfully incorporated digital nudges for healthy food promotion and which challenges still remain. We present a classification and analysis of various digital nudging strategies employed for this purpose, as well as opportunities for future research. We emphasize that various nudging techniques have the potential to support users in making healthier food choices within food recommender systems. Furthermore, user-centric evaluations represent the most effective approach for assessing the performance of these systems.
Alain D. Starke; Jutta Dierkes; Gülen Arslan Lied; Gloria Anne Babile Kasangu; Christoph Trattner
Supporting healthier food choices through AI-tailored advice: A research agenda Journal Article
In: PEC Innovation, 2025.
BibTeX | Tags: WP2: User Modeling Personalization and Engagement | Links:
@article{nokey,
title = {Supporting healthier food choices through AI-tailored advice: A research agenda},
author = {Alain D. Starke and Jutta Dierkes and Gülen Arslan Lied and Gloria Anne Babile Kasangu and Christoph Trattner},
url = {https://www.sciencedirect.com/science/article/pii/S2772628225000019},
year = {2025},
date = {2025-06-11},
journal = {PEC Innovation},
keywords = {WP2: User Modeling Personalization and Engagement},
pubstate = {published},
tppubtype = {article}
}
Sohail Ahmed Khan
Computational Visual Content Verification PhD Thesis
2025.
BibTeX | Tags: WP2: User Modeling Personalization and Engagement
@phdthesis{nokey,
title = {Computational Visual Content Verification},
author = {Sohail Ahmed Khan},
year = {2025},
date = {2025-06-03},
keywords = {WP2: User Modeling Personalization and Engagement},
pubstate = {published},
tppubtype = {phdthesis}
}
Huiling You; Samia Touileb; Lilja Øvrelid; Erik Velldal
Event-based evaluation of abstractive news summarization Conference
ACL Conference, 2025.
BibTeX | Tags: WP5: Norwegian Language Technologies | Links:
@conference{eventbased25,
title = {Event-based evaluation of abstractive news summarization},
author = {Huiling You and Samia Touileb and Lilja Øvrelid and Erik Velldal},
url = {https://mediafutures.no/63_event_based_evaluation_of_a/},
year = {2025},
date = {2025-06-02},
booktitle = {ACL Conference},
keywords = {WP5: Norwegian Language Technologies},
pubstate = {published},
tppubtype = {conference}
}
Martin Salterød Sjåvik; Samia Touileb
Ableism, Ageism, Gender, and Nationality bias in Norwegian and Multilingual Language Models Workshop
2025.
Abstract | BibTeX | Tags: WP5: Norwegian Language Technologies | Links:
@workshop{ableism25,
title = {Ableism, Ageism, Gender, and Nationality bias in Norwegian and Multilingual Language Models},
author = {Martin Salterød Sjåvik and Samia Touileb},
url = {https://mediafutures.no/ableism_ageism_gender_and_nationality_bias_in_norwegian_and_multilingual_language_models/},
year = {2025},
date = {2025-06-02},
urldate = {2025-06-02},
journal = {Workshop on Gender Bias in Natural Language Processing},
issue = {ACL Conference},
abstract = {We investigate biases related to ageism, ableism, nationality, and gender in four Norwegian and two multilingual language models. Our methodology involves using a set of templates constructed around stimuli and attributes relevant to these categories. We use statistical and predictive evaluation methods, including Kendall’s Tau correlation and dependent variable prediction rates, to assess model behaviour and output bias. Our findings indicate that models frequently associate older individuals, people with disabilities, and poorer countries with negative attributes, potentially reinforcing harmful stereotypes. However, most tested models appear to handle gender-related biases more effectively. Our findings indicate a correlation between the sentiment of the input and that of the output.},
keywords = {WP5: Norwegian Language Technologies},
pubstate = {published},
tppubtype = {workshop}
}
We investigate biases related to ageism, ableism, nationality, and gender in four Norwegian and two multilingual language models. Our methodology involves using a set of templates constructed around stimuli and attributes relevant to these categories. We use statistical and predictive evaluation methods, including Kendall’s Tau correlation and dependent variable prediction rates, to assess model behaviour and output bias. Our findings indicate that models frequently associate older individuals, people with disabilities, and poorer countries with negative attributes, potentially reinforcing harmful stereotypes. However, most tested models appear to handle gender-related biases more effectively. Our findings indicate a correlation between the sentiment of the input and that of the output.
Jia Hua Jeng; Gloria Anne Babile Kasangu; Alain D. Starke; Khadiga Seddik; Christoph Trattner
The role of GPT as an adaptive technology in climate change journalism Conference
UMAP 2025, 2025.
Abstract | BibTeX | Tags: WP2: User Modeling Personalization and Engagement | Links:
@conference{roleofGPT25,
title = {The role of GPT as an adaptive technology in climate change journalism},
author = {Jia Hua Jeng and Gloria Anne Babile Kasangu and Alain D. Starke and Khadiga Seddik and Christoph Trattner},
url = {https://mediafutures.no/umap2025-0401_small/},
year = {2025},
date = {2025-03-28},
booktitle = {UMAP 2025},
abstract = {Recent advancements in Large Language Models (LLMs), such as GPT-4o, have enabled automated content generation and adaptation, including summaries of news articles. To date, LLM use in a journalism context has been understudied, but can potentially address challenges of selective exposure and polarization by adapting content to end users. This study used a one-shot recommender platform to test whether LLM-generated news summaries were evaluated more positively than `standard' 50-word news article previews. Moreover, using climate change news from the Washington Post, we also compared the influence of different `emotional reframing' strategies to rewrite texts and their impact on the environmental behavioral intentions of end users. We used a 2 (between: Summary vs. 50-word previews) x 3 (within: fear, fear-hope or neutral reframing) research design. Participants (N = 300) were first asked to read news articles in our interface and to choose a preferred news article, while later performing an in-depth evaluation task on the usability (e.g., clarity) and trustworthiness of different framing strategies. Results showed that evaluations of summaries, while being positive, were not significantly better than those of previews. We did, however, observe that a fear-hope reframing strategy of a news article, when paired with a GPT-generated summary, led to higher pro-environmental intentions compared to neutral framing. We discuss the potential benefits of this technology.},
keywords = {WP2: User Modeling Personalization and Engagement},
pubstate = {published},
tppubtype = {conference}
}
Recent advancements in Large Language Models (LLMs), such as GPT-4o, have enabled automated content generation and adaptation, including summaries of news articles. To date, LLM use in a journalism context has been understudied, but can potentially address challenges of selective exposure and polarization by adapting content to end users. This study used a one-shot recommender platform to test whether LLM-generated news summaries were evaluated more positively than `standard' 50-word news article previews. Moreover, using climate change news from the Washington Post, we also compared the influence of different `emotional reframing' strategies to rewrite texts and their impact on the environmental behavioral intentions of end users. We used a 2 (between: Summary vs. 50-word previews) x 3 (within: fear, fear-hope or neutral reframing) research design. Participants (N = 300) were first asked to read news articles in our interface and to choose a preferred news article, while later performing an in-depth evaluation task on the usability (e.g., clarity) and trustworthiness of different framing strategies. Results showed that evaluations of summaries, while being positive, were not significantly better than those of previews. We did, however, observe that a fear-hope reframing strategy of a news article, when paired with a GPT-generated summary, led to higher pro-environmental intentions compared to neutral framing. We discuss the potential benefits of this technology.
Florence Walker; Irene Costera Meijer; Enrico Motta
Grounding and Anchoring: Success Factors for Academia-Industry Collaboration Working paper
2025.
Abstract | BibTeX | Tags: | Links:
@workingpaper{nokey,
title = {Grounding and Anchoring: Success Factors for Academia-Industry Collaboration},
author = {Florence Walker and Irene Costera Meijer and Enrico Motta},
url = {https://mediafutures.no/grounding-anchoring-aic-inline-figures-3/},
year = {2025},
date = {2025-03-14},
urldate = {2025-03-14},
abstract = {This paper integrates a top-down analysis of the existing literature on academia-industry collaboration (AIC) with a bottom-up qualitative study consisting of 16 anonymised interviews. From a variety of academic and industry sources we identified three dominant discourses that are frequently drawn upon. These often-repeated statements, while partially true, act to obscure considerably complexity and nuance. In an effort to expand existing AIC discourse, we introduce two key concepts: grounding and anchoring. These can take place in an organisational or a personal context. An AIC project is personally grounded, when individuals involved in the project feel that the work is meaningful to them, that it is aligned with their wider values and is making a difference to the world. Analogously, organisational grounding indicates that a project is consistent with the culture and values of the organisation. An AIC project is personally anchored when the people responsible for it feel a strong sense of ownership and agency towards the project. Organisational anchoring indicates that a project is fully supported within the organisation, in particular by the relevant managers and executives. When a project is sufficiently grounded and anchored, success and the personal satisfaction of the people involved in the activity are more likely to follow.
Keywords
},
keywords = {},
pubstate = {published},
tppubtype = {workingpaper}
}
This paper integrates a top-down analysis of the existing literature on academia-industry collaboration (AIC) with a bottom-up qualitative study consisting of 16 anonymised interviews. From a variety of academic and industry sources we identified three dominant discourses that are frequently drawn upon. These often-repeated statements, while partially true, act to obscure considerably complexity and nuance. In an effort to expand existing AIC discourse, we introduce two key concepts: grounding and anchoring. These can take place in an organisational or a personal context. An AIC project is personally grounded, when individuals involved in the project feel that the work is meaningful to them, that it is aligned with their wider values and is making a difference to the world. Analogously, organisational grounding indicates that a project is consistent with the culture and values of the organisation. An AIC project is personally anchored when the people responsible for it feel a strong sense of ownership and agency towards the project. Organisational anchoring indicates that a project is fully supported within the organisation, in particular by the relevant managers and executives. When a project is sufficiently grounded and anchored, success and the personal satisfaction of the people involved in the activity are more likely to follow.
Keywords
Keywords
Vladislav Mikhailov; Petter Mæhlum; Victoria Langø; Erik Velldal; Lilja Øvrelid
A Collection of Question Answering Datasets for Norwegian Proceedings Article
In: Proceedings of the joint 25th NoDaLiDa / Baltic-HLT 2025, 2025.
BibTeX | Tags: WP5: Norwegian Language Technologies | Links:
@inproceedings{nokey,
title = {A Collection of Question Answering Datasets for Norwegian},
author = {Vladislav Mikhailov and Petter Mæhlum and Victoria Langø and Erik Velldal and Lilja Øvrelid},
url = {https://arxiv.org/abs/2501.11128},
year = {2025},
date = {2025-03-03},
booktitle = {Proceedings of the joint 25th NoDaLiDa / Baltic-HLT 2025},
keywords = {WP5: Norwegian Language Technologies},
pubstate = {published},
tppubtype = {inproceedings}
}
Peter Andrews; Njål Borch; Morten Fjeld
AiModerator: A Co-Pilot for Hyper-Contextualization in Political Debate Video Conference
Intelligent User Interfaces (IUI) 2025, 2025.
Abstract | BibTeX | Tags: WP4: Media Content Interaction and Accessibility | Links:
@conference{AIMod24,
title = {AiModerator: A Co-Pilot for Hyper-Contextualization in Political Debate Video},
author = {Peter Andrews and Njål Borch and Morten Fjeld},
url = {https://mediafutures.no/iui25-78/},
year = {2025},
date = {2025-03-01},
booktitle = {Intelligent User Interfaces (IUI) 2025},
abstract = {Political debates are essential in political discourse for democratic societies. Advancements in technology have significantly transformed the structure of political debates, the ways in which politicians communicate, and the platforms through which audiences engage with them. Originally a forum for improving understanding, political debates have increasingly favored theatrics over substance, risking young adult disengagement. To bring substance back to this medium we developed AiModerator, a political debate co-pilot acting as a Multimodal Conversational Agent (MCA). AiModerator aims to promote engagement while improving understanding by analyzing video content to provide contextually relevant information. This consolidated information facilitates understanding while keeping users synchronized with the debate viewing experience. Our system builds upon multimodal techniques, integrating computer vision and large language models to demonstrate ways of improving content delivery and engagement. AiModerator's backend system extracts events from identified speech data, allowing the user to interact with these events through a touch interface on an iPad application. We address three key topics: evaluating young adults' engagement, satisfaction, and preference compared to traditional second screening, and determining whether AiModerator can improve subjective understanding. To evaluate these measures we conducted a mixed-method evaluation (n=20) within-group design A-B study. Our analysis found AiModerator excelled in promoting engagement and satisfaction while delivering clear, contextually relevant information to the user which improved their understanding of debate topics more than the second screening mode. Our qualitative analysis offers broader insights, particularly in terms of a trade-off between automation and information consolidation versus autonomy and control.},
keywords = {WP4: Media Content Interaction and Accessibility},
pubstate = {published},
tppubtype = {conference}
}
Political debates are essential in political discourse for democratic societies. Advancements in technology have significantly transformed the structure of political debates, the ways in which politicians communicate, and the platforms through which audiences engage with them. Originally a forum for improving understanding, political debates have increasingly favored theatrics over substance, risking young adult disengagement. To bring substance back to this medium we developed AiModerator, a political debate co-pilot acting as a Multimodal Conversational Agent (MCA). AiModerator aims to promote engagement while improving understanding by analyzing video content to provide contextually relevant information. This consolidated information facilitates understanding while keeping users synchronized with the debate viewing experience. Our system builds upon multimodal techniques, integrating computer vision and large language models to demonstrate ways of improving content delivery and engagement. AiModerator's backend system extracts events from identified speech data, allowing the user to interact with these events through a touch interface on an iPad application. We address three key topics: evaluating young adults' engagement, satisfaction, and preference compared to traditional second screening, and determining whether AiModerator can improve subjective understanding. To evaluate these measures we conducted a mixed-method evaluation (n=20) within-group design A-B study. Our analysis found AiModerator excelled in promoting engagement and satisfaction while delivering clear, contextually relevant information to the user which improved their understanding of debate topics more than the second screening mode. Our qualitative analysis offers broader insights, particularly in terms of a trade-off between automation and information consolidation versus autonomy and control.
Vinay Setty; Adam James Becker
Annotation Tool and Dataset for Fact-Checking Podcasts Conference
The Web Conference 2025, 2025.
Abstract | BibTeX | Tags: | Links:
@conference{annot25,
title = {Annotation Tool and Dataset for Fact-Checking Podcasts},
author = {Vinay Setty and Adam James Becker},
url = {https://mediafutures.no/2502-01402v1/},
year = {2025},
date = {2025-02-03},
booktitle = {The Web Conference 2025},
abstract = {Podcasts are a popular medium on the web, featuring diverse and multilingual content that often includes unverified claims. Fact-checking podcasts is a challenging task, requiring transcription, annotation, and claim verification, all while preserving the contextual details of spoken content. Our tool offers a novel approach to tackle these challenges by enabling real-time annotation of podcasts during playback. This unique capability allows users to listen to the podcast and annotate key elements, such as check-worthy claims, claim spans, and contextual errors, simultaneously. By integrating advanced transcription models like OpenAI's Whisper and leveraging crowdsourced annotations, we create high-quality datasets to fine-tune multilingual transformer models such as XLM-RoBERTa for tasks like claim detection and stance classification. Furthermore, we release the annotated podcast transcripts and sample annotations with preliminary experiments.},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
Podcasts are a popular medium on the web, featuring diverse and multilingual content that often includes unverified claims. Fact-checking podcasts is a challenging task, requiring transcription, annotation, and claim verification, all while preserving the contextual details of spoken content. Our tool offers a novel approach to tackle these challenges by enabling real-time annotation of podcasts during playback. This unique capability allows users to listen to the podcast and annotate key elements, such as check-worthy claims, claim spans, and contextual errors, simultaneously. By integrating advanced transcription models like OpenAI's Whisper and leveraging crowdsourced annotations, we create high-quality datasets to fine-tune multilingual transformer models such as XLM-RoBERTa for tasks like claim detection and stance classification. Furthermore, we release the annotated podcast transcripts and sample annotations with preliminary experiments.
Anastasiia Klimashevskaia; Snorre Alvsvåg; Christoph Trattner; Alain D. Starke; Astrid Tessem; Dietmar Jannach
Evaluating Sequential Recommendations in the Wild: A Case Study on Offline Accuracy, Click Rates, and Consumption Conference
ECIR 2025 conference, 2025.
Abstract | BibTeX | Tags: WP2: User Modeling Personalization and Engagement | Links:
@conference{seque25,
title = {Evaluating Sequential Recommendations in the Wild: A Case Study on Offline Accuracy, Click Rates, and Consumption},
author = {Anastasiia Klimashevskaia and Snorre Alvsvåg and Christoph Trattner and Alain D. Starke and Astrid Tessem and Dietmar Jannach},
url = {https://mediafutures.no/anastasiia__snorre___ecir_2025_camera_ready_ver_2-1/},
year = {2025},
date = {2025-02-01},
booktitle = {ECIR 2025 conference},
abstract = {Sequential recommendation problems have received increased research interest in recent years. Our knowledge about the effectiveness of sequential algorithms in practice is however limited. In this paper, we report on the outcomes of an A/B test on a video and movie streaming platform, where we benchmarked a sequential model against a non-sequential, personalized recommendation model, as well as a popularity-based baseline. Contrary to what we had expected from a preceding offline experiment, we observed that the popularity-based and the non-sequential models led to the highest click-through rates. However, in terms of the adoption of the recommendations, the sequential model was the most successful one in terms of viewing times. While our work points out the effectiveness of sequential models in practice, it also reminds us about important open challenges regarding (a) the sometimes limited predictive power of classic offline evaluations and (b) the dangers of optimizing recommendation models for click-through-rates.},
keywords = {WP2: User Modeling Personalization and Engagement},
pubstate = {published},
tppubtype = {conference}
}
Sequential recommendation problems have received increased research interest in recent years. Our knowledge about the effectiveness of sequential algorithms in practice is however limited. In this paper, we report on the outcomes of an A/B test on a video and movie streaming platform, where we benchmarked a sequential model against a non-sequential, personalized recommendation model, as well as a popularity-based baseline. Contrary to what we had expected from a preceding offline experiment, we observed that the popularity-based and the non-sequential models led to the highest click-through rates. However, in terms of the adoption of the recommendations, the sequential model was the most successful one in terms of viewing times. While our work points out the effectiveness of sequential models in practice, it also reminds us about important open challenges regarding (a) the sometimes limited predictive power of classic offline evaluations and (b) the dangers of optimizing recommendation models for click-through-rates.
Marianne Borchgrevink-Brækhus
Understanding news experience: The resonance between content, practices, and situatedness in everyday life Journal Article
In: Journalism, 2025.
Abstract | BibTeX | Tags: WP1: Understanding Media Experiences | Links:
@article{understanding25,
title = {Understanding news experience: The resonance between content, practices, and situatedness in everyday life},
author = {Marianne Borchgrevink-Brækhus},
url = {https://mediafutures.no/borchgrevink-braekhus-2025-understanding-news-experience-the-resonance-between-content-practices-and-situatedness-in/},
year = {2025},
date = {2025-01-21},
urldate = {2024-12-21},
journal = {Journalism},
abstract = {People relate to news in highly complex ways. Research on news audiences has identified how content reception, user practices, and spatiotemporal contexts influence relations to news. This study aims to see these dimensions as connected, emphasizing the significance of understanding how news content, practices, and people’s situatedness resonate in the context of everyday life and how this resonance reflects personal identity. Conceptually, the paper employs the concept of news experience as an analytical lens to understand the multilayered nature of how people relate to news. Empirically, six distinct forms of news experience are identified, all in which content, practices, and situatedness resonate differently: Reassurance, control, connection, relaxation, diversion, and stress. Drawing on a Norwegian three-step data collection, including recurring interviews, news diaries, data donations, and video-ethnography from the same informants, the article methodologically contributes to a more profound understanding of the dynamics involved in various forms of news experience.},
keywords = {WP1: Understanding Media Experiences},
pubstate = {published},
tppubtype = {article}
}
People relate to news in highly complex ways. Research on news audiences has identified how content reception, user practices, and spatiotemporal contexts influence relations to news. This study aims to see these dimensions as connected, emphasizing the significance of understanding how news content, practices, and people’s situatedness resonate in the context of everyday life and how this resonance reflects personal identity. Conceptually, the paper employs the concept of news experience as an analytical lens to understand the multilayered nature of how people relate to news. Empirically, six distinct forms of news experience are identified, all in which content, practices, and situatedness resonate differently: Reassurance, control, connection, relaxation, diversion, and stress. Drawing on a Norwegian three-step data collection, including recurring interviews, news diaries, data donations, and video-ethnography from the same informants, the article methodologically contributes to a more profound understanding of the dynamics involved in various forms of news experience.
Alain D. Starke; Sanne Vrijenhoek; Lien Michiels; Johannes Kruse; Nava Tintarev
Report on NORMalize: The Second Workshop on the Normative Design and Evaluation of Recommender Systems Workshop Forthcoming
Forthcoming.
Abstract | BibTeX | Tags: WP2: User Modeling Personalization and Engagement | Links:
@workshop{Report_Normalize25,
title = {Report on NORMalize: The Second Workshop on the Normative Design and Evaluation of Recommender Systems},
author = {Alain D. Starke and Sanne Vrijenhoek and Lien Michiels and Johannes Kruse and Nava Tintarev},
url = {https://mediafutures.no/preface-2/},
year = {2025},
date = {2025-01-19},
issue = {CEUR Vol-3898},
abstract = {Recommender systems are among the most widely used applications of artificial intelligence. Because of their widespread use, it is important that practitioners and researchers think about the impact they may have on users, society, and other stakeholders. To that effect, the NORMalize workshop seeks to introduce normative thinking, to consider the norms and values that underpin recommender systems in the recommender systems community. The objective of NORMalize is to bring together a growing community of researchers and practitioners across disciplines who want to think about the norms and values that should be considered in the design and evaluation of recommender systems, and further educate them on how to reflect on, prioritise, and operationalise such norms and values. This document is a report on the second NORMalize workshop, co-located with ACM RecSys ’24 in Bari, Italy.},
keywords = {WP2: User Modeling Personalization and Engagement},
pubstate = {forthcoming},
tppubtype = {workshop}
}
Recommender systems are among the most widely used applications of artificial intelligence. Because of their widespread use, it is important that practitioners and researchers think about the impact they may have on users, society, and other stakeholders. To that effect, the NORMalize workshop seeks to introduce normative thinking, to consider the norms and values that underpin recommender systems in the recommender systems community. The objective of NORMalize is to bring together a growing community of researchers and practitioners across disciplines who want to think about the norms and values that should be considered in the design and evaluation of recommender systems, and further educate them on how to reflect on, prioritise, and operationalise such norms and values. This document is a report on the second NORMalize workshop, co-located with ACM RecSys ’24 in Bari, Italy.
Huiling You; Samia Touileb; Erik Velldal; Lilja Øvrelid
NorEventGen: generative event extraction from Norwegian news Conference
(NoDaLiDa/Baltic-HLT 2025, 2025.
Abstract | BibTeX | Tags: WP5: Norwegian Language Technologies | Links:
@conference{norevent25,
title = {NorEventGen: generative event extraction from Norwegian news},
author = {Huiling You and Samia Touileb and Erik Velldal and Lilja Øvrelid},
url = {https://mediafutures.no/2025_nodalida_1_79/},
year = {2025},
date = {2025-01-13},
booktitle = {(NoDaLiDa/Baltic-HLT 2025},
abstract = {In this work, we approach event extraction from Norwegian news text using a
generation-based approach, which formulates the task as text-to-structure generation. We present experiments assessing the
effect of different modeling configurations
and provide an analysis of the model predictions and typical system errors. Finally,
we apply our system to a large corpus of
raw news texts and analyze the resulting
distribution of event structures in a fairly
representative snap-shot of the Norwegian
news landscape.},
keywords = {WP5: Norwegian Language Technologies},
pubstate = {published},
tppubtype = {conference}
}
In this work, we approach event extraction from Norwegian news text using a
generation-based approach, which formulates the task as text-to-structure generation. We present experiments assessing the
effect of different modeling configurations
and provide an analysis of the model predictions and typical system errors. Finally,
we apply our system to a large corpus of
raw news texts and analyze the resulting
distribution of event structures in a fairly
representative snap-shot of the Norwegian
news landscape.
generation-based approach, which formulates the task as text-to-structure generation. We present experiments assessing the
effect of different modeling configurations
and provide an analysis of the model predictions and typical system errors. Finally,
we apply our system to a large corpus of
raw news texts and analyze the resulting
distribution of event structures in a fairly
representative snap-shot of the Norwegian
news landscape.
Samia Touileb; Vladislav Mikhailov, Marie Kroka, Lilja Øvrelid, Erik Velldal
Benchmarking Abstractive Summarisation: A dataset of human-authored summaries of Norwegian news articles Conference
NoDaLiDa2025, 2025.
BibTeX | Tags: WP5: Norwegian Language Technologies | Links:
@conference{benchmark25,
title = {Benchmarking Abstractive Summarisation: A dataset of human-authored summaries of Norwegian news articles},
author = {Samia Touileb and Vladislav Mikhailov, Marie Kroka, Lilja Øvrelid, Erik Velldal},
url = {https://mediafutures.no/2501-07718v1/},
year = {2025},
date = {2025-01-13},
urldate = {2025-01-13},
booktitle = {NoDaLiDa2025},
keywords = {WP5: Norwegian Language Technologies},
pubstate = {published},
tppubtype = {conference}
}
Adane Nega Tarekegn; Bjørnar Tessem; Fazle Rabbi
A New Cluster Validation Index based on Stability Analysis Conference
ICPRAM 2025, 2025.
Abstract | BibTeX | Tags: WP3: Media Content Production and Analysis | Links:
@conference{clustervalidtio25,
title = {A New Cluster Validation Index based on Stability Analysis},
author = {Adane Nega Tarekegn and Bjørnar Tessem and Fazle Rabbi},
url = {https://mediafutures.no/133091/},
year = {2025},
date = {2025-01-01},
booktitle = {ICPRAM 2025},
journal = {ICPRAM 2025},
abstract = {Clustering is a frequently employed technique across various domains, including anomaly detection, recommender systems, video analysis, and natural language processing. Despite its broad application, validating clustering results has become one of the main challenges in cluster analysis. This can be due to factors such as the subjective nature of clustering evaluation, lack of ground truth in many real-world datasets, and sensitivity of evaluation metrics to different cluster shapes and algorithms. While there is extensive literature work in this area, developing an evaluation method that is both objective and quantitative is still challenging task requiring more effort. In this study, we proposed a new Clustering Stability Assessment Index (CSAI) that can provide a unified and quantitative approach to measure the quality and consistency of clustering solutions. The proposed CSAI validation index leverages a data resampling approach and prediction analysis to assess clustering stabil},
keywords = {WP3: Media Content Production and Analysis},
pubstate = {published},
tppubtype = {conference}
}
Clustering is a frequently employed technique across various domains, including anomaly detection, recommender systems, video analysis, and natural language processing. Despite its broad application, validating clustering results has become one of the main challenges in cluster analysis. This can be due to factors such as the subjective nature of clustering evaluation, lack of ground truth in many real-world datasets, and sensitivity of evaluation metrics to different cluster shapes and algorithms. While there is extensive literature work in this area, developing an evaluation method that is both objective and quantitative is still challenging task requiring more effort. In this study, we proposed a new Clustering Stability Assessment Index (CSAI) that can provide a unified and quantitative approach to measure the quality and consistency of clustering solutions. The proposed CSAI validation index leverages a data resampling approach and prediction analysis to assess clustering stabil
2024
Khadiga Seddik
Exploring the Ethical Challenges of AI and Recommender Systems in the Democratic Public Sphere Conference
NIKT, 2024.
Abstract | BibTeX | Tags: WP2: User Modeling Personalization and Engagement | Links:
@conference{democratpu24,
title = {Exploring the Ethical Challenges of AI and Recommender Systems in the Democratic Public Sphere},
author = {Khadiga Seddik},
url = {https://mediafutures.no/camera-ready-3/},
year = {2024},
date = {2024-11-25},
urldate = {2024-11-25},
booktitle = {NIKT},
abstract = {The rapid integration of Artificial Intelligence (AI) and Recommender Systems (RSs) into digital platforms has brought both opportunities and ethical concerns. These systems, designed to personalize content and optimize user engagement, have the potential to enhance how individuals navigate information online. However, this paper shifts the focus to the ethical complexities inherent in such systems, particularly the practice of nudging, where subtle algorithmic suggestions influence user behavior without explicit awareness. Issues like misinformation, algorithmic bias, privacy protection, and diminished content diversity raise important questions about the role of AI in shaping public discourse and decision-making processes. Rather than viewing these systems solely as tools for convenience, the paper challenges the reader to consider the deeper implications of AI-driven recommendations on democratic engagement. By examining how these technologies can quietly influence decisions and reduce exposure to different perspectives, it calls for a reevaluation of the ethical priorities in AI and RSs design. The paper calls for creating a digital space that promotes independence, fairness, and openness, making sure AI is used responsibly to support democratic values and protect user rights.},
keywords = {WP2: User Modeling Personalization and Engagement},
pubstate = {published},
tppubtype = {conference}
}
The rapid integration of Artificial Intelligence (AI) and Recommender Systems (RSs) into digital platforms has brought both opportunities and ethical concerns. These systems, designed to personalize content and optimize user engagement, have the potential to enhance how individuals navigate information online. However, this paper shifts the focus to the ethical complexities inherent in such systems, particularly the practice of nudging, where subtle algorithmic suggestions influence user behavior without explicit awareness. Issues like misinformation, algorithmic bias, privacy protection, and diminished content diversity raise important questions about the role of AI in shaping public discourse and decision-making processes. Rather than viewing these systems solely as tools for convenience, the paper challenges the reader to consider the deeper implications of AI-driven recommendations on democratic engagement. By examining how these technologies can quietly influence decisions and reduce exposure to different perspectives, it calls for a reevaluation of the ethical priorities in AI and RSs design. The paper calls for creating a digital space that promotes independence, fairness, and openness, making sure AI is used responsibly to support democratic values and protect user rights.
Khadiga Seddik
Exploring the Ethical Challenges of AI and Recommender Systems in the Democratic Public Sphere Conference
2024.
Abstract | BibTeX | Tags: WP2: User Modeling Personalization and Engagement
@conference{nokey,
title = {Exploring the Ethical Challenges of AI and Recommender Systems in the Democratic Public Sphere},
author = {Khadiga Seddik},
year = {2024},
date = {2024-11-24},
abstract = {The rapid integration of Artificial Intelligence (AI) and Recommender Systems (RSs) into digital platforms has brought both opportunities and ethical concerns. These systems, designed to personalize content and optimize user engagement, have the potential to enhance how individuals navigate information online. However, this paper shifts the focus to the ethical complexities inherent in such systems, particularly the practice of nudging, where subtle algorithmic suggestions influence user behavior without explicit awareness. Issues like misinformation, algorithmic bias, privacy protection, and diminished content diversity raise important questions about the role of AI in shaping public discourse and decision-making processes. Rather than viewing these systems solely as tools for convenience, the paper challenges the reader to consider the deeper implications of AI-driven recommendations on democratic engagement. By examining how these technologies can quietly influence decisions and reduce exposure to different perspectives, it calls for reevaluating the ethical priorities in AI and RSs design. We present the problems identified along with their potential solutions, calling for creating a digital space that promotes independence, fairness, and openness, making sure AI is used responsibly to support democratic values and protect user rights.},
keywords = {WP2: User Modeling Personalization and Engagement},
pubstate = {published},
tppubtype = {conference}
}
The rapid integration of Artificial Intelligence (AI) and Recommender Systems (RSs) into digital platforms has brought both opportunities and ethical concerns. These systems, designed to personalize content and optimize user engagement, have the potential to enhance how individuals navigate information online. However, this paper shifts the focus to the ethical complexities inherent in such systems, particularly the practice of nudging, where subtle algorithmic suggestions influence user behavior without explicit awareness. Issues like misinformation, algorithmic bias, privacy protection, and diminished content diversity raise important questions about the role of AI in shaping public discourse and decision-making processes. Rather than viewing these systems solely as tools for convenience, the paper challenges the reader to consider the deeper implications of AI-driven recommendations on democratic engagement. By examining how these technologies can quietly influence decisions and reduce exposure to different perspectives, it calls for reevaluating the ethical priorities in AI and RSs design. We present the problems identified along with their potential solutions, calling for creating a digital space that promotes independence, fairness, and openness, making sure AI is used responsibly to support democratic values and protect user rights.
Gloria Anne Babile Kasangu; Alain D. Starke; Anna Nilsen; Christoph Trattner
Picture This: How Image Filters Affect Trust in Online News Conference
Norsk IKT-konferanse for forskning og utdanning, 2024.
Abstract | BibTeX | Tags: WP2: User Modeling Personalization and Engagement
@conference{nokey,
title = {Picture This: How Image Filters Affect Trust in Online News},
author = {Gloria Anne Babile Kasangu and Alain D. Starke and Anna Nilsen and Christoph Trattner},
year = {2024},
date = {2024-11-24},
booktitle = {Norsk IKT-konferanse for forskning og utdanning},
abstract = {Users of social media platforms face concerns about the accuracy and reliability of information shared on it. This includes images being shared online, which are often linked to news events. This study investigates what effects Instagram filters
have on users’ perceived trust of online news posts that include images. Trust ratings of four different articles across four image filter conditions were obtained in an online user study (N=204). We also inquired on a user's general trust and familiarity with the news topic Also, the role of general trust and familiarity with the topic. Our analysis revealed that while Instagram filters overall may not affect perceived trust, specific visual characteristics of the filters such as brightness and
contrast affected trust levels. Additionally, individual differences in general trust and attitude towards a specific topic may influence the users’ perception of trust.},
keywords = {WP2: User Modeling Personalization and Engagement},
pubstate = {published},
tppubtype = {conference}
}
Users of social media platforms face concerns about the accuracy and reliability of information shared on it. This includes images being shared online, which are often linked to news events. This study investigates what effects Instagram filters
have on users’ perceived trust of online news posts that include images. Trust ratings of four different articles across four image filter conditions were obtained in an online user study (N=204). We also inquired on a user's general trust and familiarity with the news topic Also, the role of general trust and familiarity with the topic. Our analysis revealed that while Instagram filters overall may not affect perceived trust, specific visual characteristics of the filters such as brightness and
contrast affected trust levels. Additionally, individual differences in general trust and attitude towards a specific topic may influence the users’ perception of trust.
have on users’ perceived trust of online news posts that include images. Trust ratings of four different articles across four image filter conditions were obtained in an online user study (N=204). We also inquired on a user's general trust and familiarity with the news topic Also, the role of general trust and familiarity with the topic. Our analysis revealed that while Instagram filters overall may not affect perceived trust, specific visual characteristics of the filters such as brightness and
contrast affected trust levels. Additionally, individual differences in general trust and attitude towards a specific topic may influence the users’ perception of trust.
Ingar M Arntzen; Njål Borch; Anders Andersen
Control-driven Media. A unifying model for consistent, cross-platform multimedia experiences Journal Article
In: FTC 2024 International Journal of Advanced Computer Science and Applications (IJACSA), 2024.
Abstract | BibTeX | Tags: WP4: Media Content Interaction and Accessibility | Links:
@article{controldrivingar24,
title = {Control-driven Media. A unifying model for consistent, cross-platform multimedia experiences},
author = {Ingar M Arntzen and Njål Borch and Anders Andersen},
url = {https://mediafutures.no/preprint_cdm/},
year = {2024},
date = {2024-11-24},
journal = {FTC 2024 International Journal of Advanced Computer Science and Applications (IJACSA)},
abstract = {Targeting a diverse consumer base, many media providers offer complementary products on different platforms. Online sports coverage for instance, may include professionally produced audio and video channels, as well as Web pages and native apps offering live statistics, maps, data visualizations, social commentary and more. Many consumers are also engaging in parallel usage, setting up streaming products and interactive interfaces on available screens, laptops and handheld devices. This ability to combine products holds great promise, yet, with no coordination, cross-platform user experiences often appear inconsistent and disconnected.
We present emph{Control-driven Media (CdM)}, a new media model adding support for coordination and consistency across interfaces, devices, products and platforms, while also remaining compatible with existing services, technologies and workflows. CdM promotes online media control as an independent resource type in multimedia systems. With control as a driving force, CdM offers a highly flexible model, opening up for further innovations in automation, personalization, multi-device support, collaboration and time-driven visualization. Furthermore, CdM bridges the gap between continuous media and Web/native apps, allowing the combined powers of these platforms to be seamlessly exploited as parts of a single, consistent user experience.
CdM is supported by extensive research in time-dependent, multi-device, data-driven media experiences. In particular, State Trajectory, a unifying concept for online, timeline-consistent media control, has recently been proposed as a generic solution for media control in CdM. This paper makes the case for CdM, bringing a significant potential to the attention of research and industry.},
keywords = {WP4: Media Content Interaction and Accessibility},
pubstate = {published},
tppubtype = {article}
}
Targeting a diverse consumer base, many media providers offer complementary products on different platforms. Online sports coverage for instance, may include professionally produced audio and video channels, as well as Web pages and native apps offering live statistics, maps, data visualizations, social commentary and more. Many consumers are also engaging in parallel usage, setting up streaming products and interactive interfaces on available screens, laptops and handheld devices. This ability to combine products holds great promise, yet, with no coordination, cross-platform user experiences often appear inconsistent and disconnected.
We present emph{Control-driven Media (CdM)}, a new media model adding support for coordination and consistency across interfaces, devices, products and platforms, while also remaining compatible with existing services, technologies and workflows. CdM promotes online media control as an independent resource type in multimedia systems. With control as a driving force, CdM offers a highly flexible model, opening up for further innovations in automation, personalization, multi-device support, collaboration and time-driven visualization. Furthermore, CdM bridges the gap between continuous media and Web/native apps, allowing the combined powers of these platforms to be seamlessly exploited as parts of a single, consistent user experience.
CdM is supported by extensive research in time-dependent, multi-device, data-driven media experiences. In particular, State Trajectory, a unifying concept for online, timeline-consistent media control, has recently been proposed as a generic solution for media control in CdM. This paper makes the case for CdM, bringing a significant potential to the attention of research and industry.
We present emph{Control-driven Media (CdM)}, a new media model adding support for coordination and consistency across interfaces, devices, products and platforms, while also remaining compatible with existing services, technologies and workflows. CdM promotes online media control as an independent resource type in multimedia systems. With control as a driving force, CdM offers a highly flexible model, opening up for further innovations in automation, personalization, multi-device support, collaboration and time-driven visualization. Furthermore, CdM bridges the gap between continuous media and Web/native apps, allowing the combined powers of these platforms to be seamlessly exploited as parts of a single, consistent user experience.
CdM is supported by extensive research in time-dependent, multi-device, data-driven media experiences. In particular, State Trajectory, a unifying concept for online, timeline-consistent media control, has recently been proposed as a generic solution for media control in CdM. This paper makes the case for CdM, bringing a significant potential to the attention of research and industry.
Étienne Simon; Helene Olsen; Huiling You; Samia Touileb; Lilja Øvrelid; Erik Velldal
Generative Approaches to Event Extraction: Survey and Outlook Proceedings
2024.
Abstract | BibTeX | Tags: | Links:
@proceedings{genapproacheseven24,
title = {Generative Approaches to Event Extraction: Survey and Outlook},
author = {Étienne Simon and Helene Olsen and Huiling You and Samia Touileb and Lilja Øvrelid and Erik Velldal},
url = {https://mediafutures.no/2024-futured-1-7/},
year = {2024},
date = {2024-11-15},
issue = {ACL Anthology},
abstract = {This paper aims to map out the current landscape of generative approaches to the task of
event extraction. In surveying the emerging
literature on the topic, we identify the distinctive properties of existing studies and catalogue
them to build a comprehensive view of the various techniques employed. Finally, looking
ahead, we argue for a new generative formulation of event extraction, allowing for a better
fit between methodology and task – a proposal
that could also pertain to many other traditional
NLP tasks currently based on annotations of
text-spans.},
keywords = {},
pubstate = {published},
tppubtype = {proceedings}
}
This paper aims to map out the current landscape of generative approaches to the task of
event extraction. In surveying the emerging
literature on the topic, we identify the distinctive properties of existing studies and catalogue
them to build a comprehensive view of the various techniques employed. Finally, looking
ahead, we argue for a new generative formulation of event extraction, allowing for a better
fit between methodology and task – a proposal
that could also pertain to many other traditional
NLP tasks currently based on annotations of
text-spans.
event extraction. In surveying the emerging
literature on the topic, we identify the distinctive properties of existing studies and catalogue
them to build a comprehensive view of the various techniques employed. Finally, looking
ahead, we argue for a new generative formulation of event extraction, allowing for a better
fit between methodology and task – a proposal
that could also pertain to many other traditional
NLP tasks currently based on annotations of
text-spans.
Bilal Mahmood; Mehdi Elahi; Samia Touileb; Lubos Steskal
Can Large Language Models Support Editors Pick Related News Articles? Conference
NIKT 2024, 2024.
BibTeX | Tags: WP5: Norwegian Language Technologies | Links:
@conference{canlarge24,
title = {Can Large Language Models Support Editors Pick Related News Articles?},
author = {Bilal Mahmood and Mehdi Elahi and Samia Touileb and Lubos Steskal},
url = {https://mediafutures.no/can_large_language_models_support_editors_pick_related_news_articles____nik_2024-1/},
year = {2024},
date = {2024-11-05},
urldate = {2024-11-05},
booktitle = {NIKT 2024},
keywords = {WP5: Norwegian Language Technologies},
pubstate = {published},
tppubtype = {conference}
}
Bilal Mahmood; Mehdi Elahi; Fahrhad Vadiee; Samia Touileb; Lubos Steskal
A Supervised Machine Learning Approach for Supporting Editorial Article Selection Working paper
2024.
BibTeX | Tags: | Links:
@workingpaper{supervisedmachin24,
title = {A Supervised Machine Learning Approach for Supporting Editorial Article Selection},
author = {Bilal Mahmood and Mehdi Elahi and Fahrhad Vadiee and Samia Touileb and Lubos Steskal},
url = {https://mediafutures.no/inra_long_paper_for_workshop_2024__ml_/},
year = {2024},
date = {2024-11-05},
issue = {INRA Long paper 2024},
keywords = {},
pubstate = {published},
tppubtype = {workingpaper}
}
Enrico Motta; Francesco Osborne; Martino M. L. Pulici; Angelo Antonio Salatino; Iman Naja
Capturing the Viewpoint Dynamics in the News Domain Best Paper Book Chapter
In: pp. 18-34, Springer Nature Switzerland, Knowledge Engineering and Knowledge Management, 2024.
Abstract | BibTeX | Tags: | Links:
@inbook{capt24,
title = {Capturing the Viewpoint Dynamics in the News Domain},
author = {Enrico Motta and Francesco Osborne and Martino M. L. Pulici and Angelo Antonio Salatino and Iman Naja},
url = {https://mediafutures.no/paper_20-2/},
year = {2024},
date = {2024-11-01},
pages = {18-34},
publisher = {Springer Nature Switzerland},
edition = {Knowledge Engineering and Knowledge Management},
abstract = {Despite the seismic changes brought about by the web and social media, mainstream news sources still play a crucial role in democratic societies. In particular, a healthy democracy requires a balanced and diverse media landscape, able to provide an arena in which the various topics and viewpoints relevant to the political discourse of the day are presented and discussed. To address this issue, we have developed a hybrid human-machine approach, which uses a Large Language Model first to help analysts identify the range of viewpoints relevant to the debate around a given topic and then to classify the claims expressed in the news corpus of interest with respect to the identified viewpoints. We tested a variety of LLMs on a benchmark corpus of news items drawn from British media sources. Our results indicate that GPT4o outperforms the other alternatives and can already provide effective support for this classification task, even when run in a zero-shot learning modality.},
keywords = {},
pubstate = {published},
tppubtype = {inbook}
}
Despite the seismic changes brought about by the web and social media, mainstream news sources still play a crucial role in democratic societies. In particular, a healthy democracy requires a balanced and diverse media landscape, able to provide an arena in which the various topics and viewpoints relevant to the political discourse of the day are presented and discussed. To address this issue, we have developed a hybrid human-machine approach, which uses a Large Language Model first to help analysts identify the range of viewpoints relevant to the debate around a given topic and then to classify the claims expressed in the news corpus of interest with respect to the identified viewpoints. We tested a variety of LLMs on a benchmark corpus of news items drawn from British media sources. Our results indicate that GPT4o outperforms the other alternatives and can already provide effective support for this classification task, even when run in a zero-shot learning modality.
Jia Hua Jeng; Gloria Anne Babile Kasangu; Alain D. Starke; Erik Knudsen; Christoph Trattner
Negativity Sells? Using an LLM to Affectively Reframe News Articles in a Recommender System Workshop
2024.
Abstract | BibTeX | Tags: WP2: User Modeling Personalization and Engagement | Links:
@workshop{negativ24,
title = {Negativity Sells? Using an LLM to Affectively Reframe News Articles in a Recommender System},
author = {Jia Hua Jeng and Gloria Anne Babile Kasangu and Alain D. Starke and Erik Knudsen and Christoph Trattner},
url = {https://mediafutures.no/inra_jeng/},
year = {2024},
date = {2024-10-30},
issue = {RecSys2024 - INRA workshop},
abstract = {Recent developments in artificial intelligence allow newsrooms to automate journalistic choices and processes. In doing so, news framing can impact people's engagement with news media, as well as their willingness to pay for news articles. Large Language Models (LLMs) can be used as a framing tool, aligning headlines with a news website user's preferences or state. It is, however, unknown how users perceive and experience the use of a platform with such LLM-reframed news headlines. We present the results of a user study (N = 300) with a news recommender system (NRS). Users had to read three news articles from The Washington Post from a preferred category (abortion, economics, gun control). Headlines were rewritten by an LLM (ChatGPT-4) and images were replaced in specific affective styles, across 2 (positive or negative headlines) x 3 (positive or negative image, or no image) between-subject framing conditions. We found that negatively framed images and text elicited negative emotions, while positive framing had little effect. Users were also more willing to pay for a news service when facing negatively framed headlines and images. Surprisingly, the congruency between text and image (i.e., both being framed negatively or positively) did not significantly impact engagement. We discuss how this study can shape further research on affective framing in news recommender systems and how such applications could impact journalism practices.},
keywords = {WP2: User Modeling Personalization and Engagement},
pubstate = {published},
tppubtype = {workshop}
}
Recent developments in artificial intelligence allow newsrooms to automate journalistic choices and processes. In doing so, news framing can impact people's engagement with news media, as well as their willingness to pay for news articles. Large Language Models (LLMs) can be used as a framing tool, aligning headlines with a news website user's preferences or state. It is, however, unknown how users perceive and experience the use of a platform with such LLM-reframed news headlines. We present the results of a user study (N = 300) with a news recommender system (NRS). Users had to read three news articles from The Washington Post from a preferred category (abortion, economics, gun control). Headlines were rewritten by an LLM (ChatGPT-4) and images were replaced in specific affective styles, across 2 (positive or negative headlines) x 3 (positive or negative image, or no image) between-subject framing conditions. We found that negatively framed images and text elicited negative emotions, while positive framing had little effect. Users were also more willing to pay for a news service when facing negatively framed headlines and images. Surprisingly, the congruency between text and image (i.e., both being framed negatively or positively) did not significantly impact engagement. We discuss how this study can shape further research on affective framing in news recommender systems and how such applications could impact journalism practices.
Jia Hua Jeng
Bridging Viewpoints in News with Recommender Systems Conference
ACM RecSys2024, 2024.
Abstract | BibTeX | Tags: WP2: User Modeling Personalization and Engagement | Links:
@conference{bridging24,
title = {Bridging Viewpoints in News with Recommender Systems},
author = {Jia Hua Jeng},
url = {https://mediafutures.no/recsys24-phd/},
year = {2024},
date = {2024-10-08},
urldate = {2024-10-08},
booktitle = {ACM RecSys2024},
abstract = {News Recommender systems (NRSs) aid in decision-making in news media. However, undesired effects can emerge. Among these are selective exposures that may contribute to polarization, potentially reinforcing existing attitudes through belief perseverance—discounting contrary evidence due to their opposing attitudinal strength. This can be unsafe for people, making it difficult to accept information objectively. A crucial issue in news recommender system research is how to mitigate these undesired effects by designing recommender interfaces and machine learning models that enable people to consider to be more open to different perspectives. Alongside accurate models, the user experience is an equally important measure. Indeed, the core statistics are based on users’ behaviors and experiences in this research project. Therefore, this research agenda aims to steer the choices of readers' based on altering their attitudes. The core methods plan to concentrate on the interface design and ML model building involving manipulations of cues, users’ behaviors prediction, NRSs algorithm and changing the nudges. In sum, the project aims to provide insight in the extent to which news recommender systems can be effective in mitigating polarized opinions.},
keywords = {WP2: User Modeling Personalization and Engagement},
pubstate = {published},
tppubtype = {conference}
}
News Recommender systems (NRSs) aid in decision-making in news media. However, undesired effects can emerge. Among these are selective exposures that may contribute to polarization, potentially reinforcing existing attitudes through belief perseverance—discounting contrary evidence due to their opposing attitudinal strength. This can be unsafe for people, making it difficult to accept information objectively. A crucial issue in news recommender system research is how to mitigate these undesired effects by designing recommender interfaces and machine learning models that enable people to consider to be more open to different perspectives. Alongside accurate models, the user experience is an equally important measure. Indeed, the core statistics are based on users’ behaviors and experiences in this research project. Therefore, this research agenda aims to steer the choices of readers' based on altering their attitudes. The core methods plan to concentrate on the interface design and ML model building involving manipulations of cues, users’ behaviors prediction, NRSs algorithm and changing the nudges. In sum, the project aims to provide insight in the extent to which news recommender systems can be effective in mitigating polarized opinions.
Anastasiia Klimashevskaia; Mehdi Elahi; Dietmar Jannach; Christoph Trattner; Simen Buodd
Empowering Editors: How Automated Recommendations Support Editorial Article Curation Workshop
RecSys 2024, INRA workshop, 2024.
Abstract | BibTeX | Tags: | Links:
@workshop{empoweringANA24,
title = {Empowering Editors: How Automated Recommendations Support Editorial Article Curation},
author = {Anastasiia Klimashevskaia and Mehdi Elahi and Dietmar Jannach and Christoph Trattner and Simen Buodd},
url = {https://mediafutures.no/recsys2024-workshops_paper_119-3/},
year = {2024},
date = {2024-10-01},
urldate = {2024-10-01},
booktitle = {RecSys 2024, INRA workshop},
issue = {RecSys 2024, INRA workshop},
abstract = {The application of recommender systems in the news domain has experienced rapid growth in recent years. Various news outlets are proposing a full automation of a newspaper front page through automated recommendation. In this paper, however, we explore the synergy of editorial and algorithmic news curation by analyzing the front page of a real-world news platform, where news articles are either selected automatically by a recommendation algorithm or are selected manually by editors. An investigation of the interaction log data from an online newspaper revealed that while the editorial staff is focusing on content that is generally popular across large parts of the audience, the algorithmic curation can, in addition, provide small yet noteworthy personalization touches for individual readers. The results of the analysis demonstrate an example of a successful coexistence of editorial and algorithmic news curation.},
howpublished = {RecSys 2024, INRA workshop},
keywords = {},
pubstate = {published},
tppubtype = {workshop}
}
The application of recommender systems in the news domain has experienced rapid growth in recent years. Various news outlets are proposing a full automation of a newspaper front page through automated recommendation. In this paper, however, we explore the synergy of editorial and algorithmic news curation by analyzing the front page of a real-world news platform, where news articles are either selected automatically by a recommendation algorithm or are selected manually by editors. An investigation of the interaction log data from an online newspaper revealed that while the editorial staff is focusing on content that is generally popular across large parts of the audience, the algorithmic curation can, in addition, provide small yet noteworthy personalization touches for individual readers. The results of the analysis demonstrate an example of a successful coexistence of editorial and algorithmic news curation.
Fazle Rabbi
A Model-Based Framework for Exploring Conflict Dynamics Workshop
2024.
Abstract | BibTeX | Tags: WP3: Media Content Production and Analysis | Links:
@workshop{conflict24,
title = {A Model-Based Framework for Exploring Conflict Dynamics},
author = {Fazle Rabbi},
url = {https://mediafutures.no/3652620-3688206-2/},
year = {2024},
date = {2024-09-24},
issue = {ACM/IEEE },
abstract = {This paper introduces a novel framework for conflict analysis that leverages advanced visual modeling techniques. By employing comparative analysis, key variables influencing armed conflicts are identified and analyzed. The framework includes a meta-model representing domain concepts such as the goals and strategies of conflicting parties, escalating stages, and impacts of conflicts. Conflict escalation is a complex process characterized by interactions between opposing parties. This paper presents a structured model that outlines how conflicts evolve and intensify over time. We adapt a meta-modeling framework called the Diagram Predicate Framework (DPF) to represent conflict-related concepts and extend it to support abstract view generation. This framework facilitates the analysis of conflict trends and the study of dynamics across various levels of abstraction. A computational model based on category theory is proposed for trend analysis, enabling the extraction of patterns of conflict evolution and the comparison of strategies and goals at different escalation stages. Categorical operations such as pullback and limit construction are employed to compute conflict evolution and identify common structures among conflict instances, providing insights into conflict dynamics across diverse zones.},
keywords = {WP3: Media Content Production and Analysis},
pubstate = {published},
tppubtype = {workshop}
}
This paper introduces a novel framework for conflict analysis that leverages advanced visual modeling techniques. By employing comparative analysis, key variables influencing armed conflicts are identified and analyzed. The framework includes a meta-model representing domain concepts such as the goals and strategies of conflicting parties, escalating stages, and impacts of conflicts. Conflict escalation is a complex process characterized by interactions between opposing parties. This paper presents a structured model that outlines how conflicts evolve and intensify over time. We adapt a meta-modeling framework called the Diagram Predicate Framework (DPF) to represent conflict-related concepts and extend it to support abstract view generation. This framework facilitates the analysis of conflict trends and the study of dynamics across various levels of abstraction. A computational model based on category theory is proposed for trend analysis, enabling the extraction of patterns of conflict evolution and the comparison of strategies and goals at different escalation stages. Categorical operations such as pullback and limit construction are employed to compute conflict evolution and identify common structures among conflict instances, providing insights into conflict dynamics across diverse zones.
Ayoub El Majjodi; Sohail Ahmed Khan; Alain D. Starke; Mehdi Elahi; Christoph Trattner
Advancing Visual Food Attractiveness Predictions for Healthy Food Recommender Systems Conference
The ACM Conference on Recommender Systems (RecSys) 2024, 2024.
Abstract | BibTeX | Tags: | Links:
@conference{visualfood24,
title = {Advancing Visual Food Attractiveness Predictions for Healthy Food Recommender Systems},
author = {Ayoub El Majjodi and Sohail Ahmed Khan and Alain D. Starke and Mehdi Elahi and Christoph Trattner},
url = {https://mediafutures.no/healthrecsys-2024-ayoub-1/},
year = {2024},
date = {2024-09-17},
booktitle = {The ACM Conference on Recommender Systems (RecSys) 2024},
abstract = {The visual representation of food has a significant influence on
how people choose food in the real world but also in a digital food
recommender scenario. Previous studies on that matter show that
small change in visual features can change human decision-making,
regardless of whether the food is healthy or not. This paper reports
on a study that aims to understand further how users perceive
the attractiveness of food images in the digital world. In an online
mixed-methods survey (N=192), users provided visual attractive-
ness ratings on a 7-point scale and provided textual assessments
of the visual attractiveness of food images. We found a robust
correlation between fundamental visual features (e.g., contrast, col-
orfulness) and perceived image attractiveness. The analysis also
revealed that cooking skills predicted food image attractiveness
among user factors. Regarding food image dimensions, appearance
and perceived healthiness emerged to be significantly correlated
with user ratings for food image attractiveness.},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
The visual representation of food has a significant influence on
how people choose food in the real world but also in a digital food
recommender scenario. Previous studies on that matter show that
small change in visual features can change human decision-making,
regardless of whether the food is healthy or not. This paper reports
on a study that aims to understand further how users perceive
the attractiveness of food images in the digital world. In an online
mixed-methods survey (N=192), users provided visual attractive-
ness ratings on a 7-point scale and provided textual assessments
of the visual attractiveness of food images. We found a robust
correlation between fundamental visual features (e.g., contrast, col-
orfulness) and perceived image attractiveness. The analysis also
revealed that cooking skills predicted food image attractiveness
among user factors. Regarding food image dimensions, appearance
and perceived healthiness emerged to be significantly correlated
with user ratings for food image attractiveness.
how people choose food in the real world but also in a digital food
recommender scenario. Previous studies on that matter show that
small change in visual features can change human decision-making,
regardless of whether the food is healthy or not. This paper reports
on a study that aims to understand further how users perceive
the attractiveness of food images in the digital world. In an online
mixed-methods survey (N=192), users provided visual attractive-
ness ratings on a 7-point scale and provided textual assessments
of the visual attractiveness of food images. We found a robust
correlation between fundamental visual features (e.g., contrast, col-
orfulness) and perceived image attractiveness. The analysis also
revealed that cooking skills predicted food image attractiveness
among user factors. Regarding food image dimensions, appearance
and perceived healthiness emerged to be significantly correlated
with user ratings for food image attractiveness.
Peter Røysland Aarnes; Vinay Setty; Petra Galuščáková
IAI Group at CheckThat! 2024: Transformer Models and Data Augmentation for Checkworthy Claim Detection Conference
Conference and Labs of the Evaluation Forum, 2024.
Abstract | BibTeX | Tags: WP3: Media Content Production and Analysis | Links:
@conference{checkthat24,
title = {IAI Group at CheckThat! 2024: Transformer Models and Data Augmentation for Checkworthy Claim Detection},
author = {Peter Røysland Aarnes and Vinay Setty and Petra Galuščáková},
url = {https://mediafutures.no/checkthat-lab-task-1-notebook/},
year = {2024},
date = {2024-09-13},
urldate = {2024-09-13},
booktitle = {Conference and Labs of the Evaluation Forum},
abstract = {This paper describes IAI group’s participation for automated check-worthiness estimation for claims, within
the framework of the 2024 CheckThat! Lab “Task 1: Check-Worthiness Estimation”. The task involves the
automated detection of check-worthy claims in English, Dutch, and Arabic political debates and Twitter data. We
utilized various pre-trained generative decoder and encoder transformer models, employing methods such as
few-shot chain-of-thought reasoning, fine-tuning, data augmentation, and transfer learning from one language
to another. Despite variable success in terms of performance, our models achieved notable placements on the
organizer’s leaderboard: ninth-best in English, third-best in Dutch, and the top placement in Arabic, utilizing
multilingual datasets for enhancing the generalizability of check-worthiness detection. Despite a significant drop
in performance on the unlabeled test dataset compared to the development test dataset, our findings contribute
to the ongoing efforts in claim detection research, highlighting the challenges and potential of language-specific
adaptations in claim verification systems.},
keywords = {WP3: Media Content Production and Analysis},
pubstate = {published},
tppubtype = {conference}
}
This paper describes IAI group’s participation for automated check-worthiness estimation for claims, within
the framework of the 2024 CheckThat! Lab “Task 1: Check-Worthiness Estimation”. The task involves the
automated detection of check-worthy claims in English, Dutch, and Arabic political debates and Twitter data. We
utilized various pre-trained generative decoder and encoder transformer models, employing methods such as
few-shot chain-of-thought reasoning, fine-tuning, data augmentation, and transfer learning from one language
to another. Despite variable success in terms of performance, our models achieved notable placements on the
organizer’s leaderboard: ninth-best in English, third-best in Dutch, and the top placement in Arabic, utilizing
multilingual datasets for enhancing the generalizability of check-worthiness detection. Despite a significant drop
in performance on the unlabeled test dataset compared to the development test dataset, our findings contribute
to the ongoing efforts in claim detection research, highlighting the challenges and potential of language-specific
adaptations in claim verification systems.
the framework of the 2024 CheckThat! Lab “Task 1: Check-Worthiness Estimation”. The task involves the
automated detection of check-worthy claims in English, Dutch, and Arabic political debates and Twitter data. We
utilized various pre-trained generative decoder and encoder transformer models, employing methods such as
few-shot chain-of-thought reasoning, fine-tuning, data augmentation, and transfer learning from one language
to another. Despite variable success in terms of performance, our models achieved notable placements on the
organizer’s leaderboard: ninth-best in English, third-best in Dutch, and the top placement in Arabic, utilizing
multilingual datasets for enhancing the generalizability of check-worthiness detection. Despite a significant drop
in performance on the unlabeled test dataset compared to the development test dataset, our findings contribute
to the ongoing efforts in claim detection research, highlighting the challenges and potential of language-specific
adaptations in claim verification systems.
Laurence Dierickx; Arjen van Dalen; Andreas L. Opdahl; Carl-Gustav Linden
Striking the Balance in Using LLMs for Fact-Checking: A Narrative Literature Review Conference
Multidisciplinary International Symposium on Disinformation in Open Online Media, Springer Nature , 2024.
BibTeX | Tags: WP3: Media Content Production and Analysis | Links:
@conference{nokey,
title = {Striking the Balance in Using LLMs for Fact-Checking: A Narrative Literature Review},
author = {Laurence Dierickx and Arjen van Dalen and Andreas L. Opdahl and Carl-Gustav Linden},
url = {https://link.springer.com/chapter/10.1007/978-3-031-71210-4_1},
year = {2024},
date = {2024-08-31},
urldate = {2025-08-31},
booktitle = {Multidisciplinary International Symposium on Disinformation in Open Online Media},
pages = {1-15},
publisher = {Springer Nature },
keywords = {WP3: Media Content Production and Analysis},
pubstate = {published},
tppubtype = {conference}
}
Peter Røysland Aarnes; Vinay Setty; Petra Galuščáková
Transformer models and data augmentation for checkworthy claim detection Journal Article
In: CLEF2024 CheckThat!, 2024.
BibTeX | Tags: WP3: Media Content Production and Analysis | Links:
@article{nokey,
title = {Transformer models and data augmentation for checkworthy claim detection},
author = {Peter Røysland Aarnes and Vinay Setty and Petra Galuščáková},
url = {https://arxiv.org/abs/2408.01118},
year = {2024},
date = {2024-08-02},
urldate = {2025-08-02},
journal = {CLEF2024 CheckThat!},
keywords = {WP3: Media Content Production and Analysis},
pubstate = {published},
tppubtype = {article}
}
Alain D. Starke; Vegard R. Solberg; Sebastian Øverhaug Larsen; Christoph Trattner
Examining the Merits of Feature-specific Similarity Functions in the News Domain using Human Judgments Journal Article
In: User Modeling and User-Adapted Interaction, 2024.
Abstract | BibTeX | Tags: WP2: User Modeling Personalization and Engagement | Links:
@article{NewsDomainusing24,
title = {Examining the Merits of Feature-specific Similarity Functions in the News Domain using Human Judgments},
author = {Alain D. Starke and Vegard R. Solberg and Sebastian Øverhaug Larsen and Christoph Trattner},
url = {https://mediafutures.no/umuai_special_issue_news_similarity-1/},
year = {2024},
date = {2024-07-27},
urldate = {2024-07-27},
journal = {User Modeling and User-Adapted Interaction},
abstract = {Online news article recommendations are typically of the ‘more like this’ type, generated by similarity functions. Across three studies, we examined the representativeness of different similarity functions for news item retrieval, by comparing them to human judgments of similarity. In Study 1 (N = 401), participants assessed the overall similarity of ten randomly paired news articles on politics, and compared their judgments to different feature-specific similarity functions (e.g., based on body text or images). In Study 2, we checked for domain differences in a mixed-methods survey (N = 45), surfacing evidence that the effectiveness of similarity functions differs across different news categories (‘Recent Events’, ‘Sport’). In Study 3 (N = 173), we improved the design of Study 1, by controlling for how news articles were matched, differentiating between dissimilar news articles and articles that were matched on a shared topic, named entities, and/or date of publication, across ‘Recent Events’ and ‘Sport’ categories.
Across all studies, we found that users mostly used text-based features (e.g., body text, title) for their similarity judgments, while BodyText:TF-IDF was found to be the most representative for their judgments. Moreover, the strength of similarity judgments by humans and similarity scores by feature-specific functions was strongly affected
by how news article pairs were matched. We show that humans and similarity functions are better aligned when two news articles are more alike, such as in a news recommendation scenario.},
keywords = {WP2: User Modeling Personalization and Engagement},
pubstate = {published},
tppubtype = {article}
}
Online news article recommendations are typically of the ‘more like this’ type, generated by similarity functions. Across three studies, we examined the representativeness of different similarity functions for news item retrieval, by comparing them to human judgments of similarity. In Study 1 (N = 401), participants assessed the overall similarity of ten randomly paired news articles on politics, and compared their judgments to different feature-specific similarity functions (e.g., based on body text or images). In Study 2, we checked for domain differences in a mixed-methods survey (N = 45), surfacing evidence that the effectiveness of similarity functions differs across different news categories (‘Recent Events’, ‘Sport’). In Study 3 (N = 173), we improved the design of Study 1, by controlling for how news articles were matched, differentiating between dissimilar news articles and articles that were matched on a shared topic, named entities, and/or date of publication, across ‘Recent Events’ and ‘Sport’ categories.
Across all studies, we found that users mostly used text-based features (e.g., body text, title) for their similarity judgments, while BodyText:TF-IDF was found to be the most representative for their judgments. Moreover, the strength of similarity judgments by humans and similarity scores by feature-specific functions was strongly affected
by how news article pairs were matched. We show that humans and similarity functions are better aligned when two news articles are more alike, such as in a news recommendation scenario.
Across all studies, we found that users mostly used text-based features (e.g., body text, title) for their similarity judgments, while BodyText:TF-IDF was found to be the most representative for their judgments. Moreover, the strength of similarity judgments by humans and similarity scores by feature-specific functions was strongly affected
by how news article pairs were matched. We show that humans and similarity functions are better aligned when two news articles are more alike, such as in a news recommendation scenario.
Bilal Mahmood; Mehdi Elahi; Samia Touileb; Lubos Steskal; Christoph Trattner
Incorporating Editorial Feedback in the Evaluation of News Recommender Systems Conference
ACM UMAP 2024, 2024.
Abstract | BibTeX | Tags: WP2: User Modeling Personalization and Engagement | Links:
@conference{incoed24,
title = {Incorporating Editorial Feedback in the Evaluation of News Recommender Systems},
author = {Bilal Mahmood and Mehdi Elahi and Samia Touileb and Lubos Steskal and Christoph Trattner},
url = {https://mediafutures.no/lbr_umap_editorial_component_in_nrs/},
year = {2024},
date = {2024-07-01},
urldate = {2024-07-01},
booktitle = {ACM UMAP 2024},
abstract = {Research in the recommender systems field typically applies a rather traditional evaluation methodology when assessing the quality of recommendations. This methodology heavily relies on incorporating different forms of user feedback (e.g., clicks) representing the specific needs and interests of the users. While this methodology may offer various benefits, it may fail to comprehensively project the complexities of certain application domains, such as the news domain. This domain is distinct from other domains primarily due to the strong influence of editorial control in the news delivery process. Incorporation of this role can profoundly impact how the relevance of news articles is measured when recommended to the users. Despite its critical importance, there appears to be a research gap in investigating the dynamics between the roles of editorial control and personalization in the community of recommender systems. In this paper, we address this gap by conducting experiments where the relevance of recommendations is assessed from an editorial perspective. We received a real-world dataset from TV 2, one of the largest editor-managed commercial media houses in Norway, which includes editors’ feedback on how news articles are being related. In our experiment, we considered a scenario where algorithm-generated recommendations, using the K-Nearest Neighbor (KNN) model, employing various text embedding models to encode different sections of the news articles (e.g., title, lead title, body text, and full text), are compared against the editorial feedback. The results are promising, demonstrating the effectiveness of the recommendation in fulfilling the editorial prospects.},
keywords = {WP2: User Modeling Personalization and Engagement},
pubstate = {published},
tppubtype = {conference}
}
Research in the recommender systems field typically applies a rather traditional evaluation methodology when assessing the quality of recommendations. This methodology heavily relies on incorporating different forms of user feedback (e.g., clicks) representing the specific needs and interests of the users. While this methodology may offer various benefits, it may fail to comprehensively project the complexities of certain application domains, such as the news domain. This domain is distinct from other domains primarily due to the strong influence of editorial control in the news delivery process. Incorporation of this role can profoundly impact how the relevance of news articles is measured when recommended to the users. Despite its critical importance, there appears to be a research gap in investigating the dynamics between the roles of editorial control and personalization in the community of recommender systems. In this paper, we address this gap by conducting experiments where the relevance of recommendations is assessed from an editorial perspective. We received a real-world dataset from TV 2, one of the largest editor-managed commercial media houses in Norway, which includes editors’ feedback on how news articles are being related. In our experiment, we considered a scenario where algorithm-generated recommendations, using the K-Nearest Neighbor (KNN) model, employing various text embedding models to encode different sections of the news articles (e.g., title, lead title, body text, and full text), are compared against the editorial feedback. The results are promising, demonstrating the effectiveness of the recommendation in fulfilling the editorial prospects.
Alain D. Starke; Anders Sandvik Bremnes; Erik Knudsen; Damian Trilling; Christoph Trattner
Perception versus Reality: Evaluating User Awareness of Political Selective Exposure in News Recommender Systems Conference
ACM UMAP 2024, 2024.
Abstract | BibTeX | Tags: WP2: User Modeling Personalization and Engagement | Links:
@conference{percepvsreal24,
title = {Perception versus Reality: Evaluating User Awareness of Political Selective Exposure in News Recommender Systems},
author = {Alain D. Starke and Anders Sandvik Bremnes and Erik Knudsen and Damian Trilling and Christoph Trattner},
url = {https://mediafutures.no/umap2024___erik_alain_damian_anders_christoph/},
year = {2024},
date = {2024-07-01},
urldate = {2024-07-01},
booktitle = {ACM UMAP 2024},
abstract = {News Recommender Systems (NRSs) have become increasingly pivotal in shaping the news landscape, particularly in how news is disseminated. This has also led to concerns about information diversity, especially regarding selective exposure in the realm of political news. Users may not recognize that news content presented to them is subject to selective exposure, through users that incorporate political beliefs. Within the U.S. two-party system, our research explores the interactions between NRSs and users’ ability to discern news articles that align with their political biases. We performed an online experiment (N = 160) to address the issue of user awareness and self-recognition of selective exposure within NRSs. Users were asked to select any number of news articles that matched their political orientation (i.e., Democrat or Republican) from a list of 50 news articles (5 Democrat, 5 Republican, 40 filler articles), which were either ranked saliently towards their political orientation or randomly. Contrary to expectations, our findings reveal no significant difference in article selection between participants exposed to a baseline random order and those who where presented with the more salient and easy to select version. We did observe that Republicans performed worse than Democrats in identifying aligning articles, based on precision and recall metrics.},
keywords = {WP2: User Modeling Personalization and Engagement},
pubstate = {published},
tppubtype = {conference}
}
News Recommender Systems (NRSs) have become increasingly pivotal in shaping the news landscape, particularly in how news is disseminated. This has also led to concerns about information diversity, especially regarding selective exposure in the realm of political news. Users may not recognize that news content presented to them is subject to selective exposure, through users that incorporate political beliefs. Within the U.S. two-party system, our research explores the interactions between NRSs and users’ ability to discern news articles that align with their political biases. We performed an online experiment (N = 160) to address the issue of user awareness and self-recognition of selective exposure within NRSs. Users were asked to select any number of news articles that matched their political orientation (i.e., Democrat or Republican) from a list of 50 news articles (5 Democrat, 5 Republican, 40 filler articles), which were either ranked saliently towards their political orientation or randomly. Contrary to expectations, our findings reveal no significant difference in article selection between participants exposed to a baseline random order and those who where presented with the more salient and easy to select version. We did observe that Republicans performed worse than Democrats in identifying aligning articles, based on precision and recall metrics.
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