/ Introduction
/ Introduction
/ Introduction
Recommendation enables media applications to support users in discovering additional media content (e.g., news articles and videos) and to keep consumers engaged. The main challenge in this context is that some recommendation approaches may have little potential for the discovery of new types of content for the consumer, and they might cause the popular media content to become even more popular. Such problems can ultimately lead to filter bubbles, echo chambers, or groupthink conditions. The research stream will tackle these undesired phenomena, which are likely to originate from current personalization and recommendation approaches.
This will be done by computing responsible (predictive) models for fair recommendations that will enhance user engagement through novel mechanisms by (i) providing explanations of recommendations to users (transparency), (ii) expanding recommendations to cover a rich spectrum of media content (diversity), (iii) ensuring that niche or minority content is suggested to users (fairness).
In addition, as media users face a media environment that is increasingly perceived as fragmented, understanding users’ trust in and use of media is crucial to democracy, as media use continues to be central for citizens’ information about and engagement in society.
New knowledge: The outcome will be novel recommendation algorithms taking into account multiple competing objectives (e.g., relevance vs. information balance). In doing so, the research stream will address the following main research questions: To what extent can we effectively and fairly model online user behaviour and predict this behaviour? To what extent can we personalize and engage users online to efficiently keep them informed, and at the same time do this responsibly?
Objective: To develop user modeling and personalisation techniques capable of effectively eliciting user preferences in order to enchance the user experience when interacting with media content while taking into account important competing factors (e.g., business values, societal values, individual values).
With the datafication of everyday life, increasingly powerful platforms and intensified competition for attention, media users face a media environment which is increasingly perceived as intrusive and exploitative of their data traces. This situation causes ambivalence and resignation as well as immersive and joyful media experiences. Understanding these experiences is crucial to democracy, as media use continues to be central for public connection and citizens’ information about and engagement in society. In addition to making sense of media usage through metrics such as clicks, time spent, shares or comments, critical attention to problematic representations of datafication should be bridged with broader and deeper understandings of media as experience.
New knowledge: The collaboration between Bergen Media Use Research Group at UiB and user partners in the centre will generate new knowledge from a dual strategy: (i) monitor users across media with state-of-the-art tracking devices and critical attention to limitations of such methods, combined with surveys and survey experiments, and (ii) understand future media experiences through qualitative in-depth explorations of emerging and transforming media use
Objective: To provide fundamental knowledge on how users will interact with the media of the future, by monitoring and understanding users across media through advanced quantitative and qualitative approaches.
With the datafication of everyday life, increasingly powerful platforms and intensified competition for attention, media users face a media environment which is increasingly perceived as intrusive and exploitative of their data traces. This situation causes ambivalence and resignation as well as immersive and joyful media experiences. Understanding these experiences is crucial to democracy, as media use continues to be central for public connection and citizens’ information about and engagement in society. In addition to making sense of media usage through metrics such as clicks, time spent, shares or comments, critical attention to problematic representations of datafication should be bridged with broader and deeper understandings of media as experience.
New knowledge: The collaboration between Bergen Media Use Research Group at UiB and user partners in the centre will generate new knowledge from a dual strategy: (i) monitor users across media with state-of-the-art tracking devices and critical attention to limitations of such methods, combined with surveys and survey experiments, and (ii) understand future media experiences through qualitative in-depth explorations of emerging and transforming media use
Objective: To provide fundamental knowledge on how users will interact with the media of the future, by monitoring and understanding users across media through advanced quantitative and qualitative approaches.
/ People
Snorre Alvsvåg
Industry Leader
TV2
Read more
Dietmar Jannach
Advisor & Key Researcher
Universität Klagenfurt
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Svenja Lys Forstner
PhD Candidate
University of Bergen
Read more
Erik Knudsen
Researcher
University of Bergen
Bilal Mahmood
PhD Candidate
University of Bergen
Anja Svartberg
NRK
/ Publications
2026
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.
@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 = {},
pubstate = {forthcoming},
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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
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.
@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 = {},
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.
@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 = {},
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.
@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 = {},
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.
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 | 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 = {},
pubstate = {published},
tppubtype = {inbook}
}
John Magnus Ragnhildson Dahl
Teen Boys and their Smartphones as Worldmaking Devices: In the Palm of their Hands Book
Palgrave MacMillan, 2025.
@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 = {},
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.
@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 = {},
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 | 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 = {},
pubstate = {published},
tppubtype = {article}
}
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.
@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 = {},
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.
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.
@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 = {},
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.
@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 = {},
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.
@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 = {},
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.
2024
Khadiga Seddik
Exploring the Ethical Challenges of AI and Recommender Systems in the Democratic Public Sphere Conference
NIKT, 2024.
@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 = {},
pubstate = {published},
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}
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.
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.
@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 = {},
pubstate = {published},
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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.
Khadiga Seddik
Exploring the Ethical Challenges of AI and Recommender Systems in the Democratic Public Sphere Conference
2024.
@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 = {},
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.
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.
@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 = {},
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.
@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 = {},
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.
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.
@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 = {},
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.
@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 = {},
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.
Daniel Rosnes; Alain Starke; Christoph Trattner
Shaping the Future of Content-based News Recommenders: Insights from Evaluating Feature-Specific Similarity Metrics Conference
ACM UMAP '24, 2024.
@conference{umap2024Daniel,
title = {Shaping the Future of Content-based News Recommenders: Insights from Evaluating Feature-Specific Similarity Metrics},
author = {Daniel Rosnes and Alain Starke and Christoph Trattner },
url = {https://mediafutures.no/umap2024/},
year = {2024},
date = {2024-07-01},
booktitle = {ACM UMAP '24},
abstract = {In news media, recommender system technology faces several domain-specific challenges. The continuous stream of new content and users deems content-based recommendation strategies, based on similar-item retrieval, to remain popular. However, a persistent challenge is to select relevant features and corresponding similarity functions, and whether this depends on the specific context. We evaluated feature-specific similarity metrics using human similarity judgments across national and local news domains. We performed an online experiment ($N = 141$) where we asked participants to judge the similarity between pairs of randomly sampled news articles. We had three contributions: (1) comparing novel metrics based on large language models to ones traditionally used in news recommendations, (2) exploring differences in similarity judgments across national and local news domains, and (3) examining which content-based strategies were perceived as appropriate in the news domain. Our results showed that one of the novel large language model based metrics (SBERT) was highly correlated with human judgments, while there were only small, most non-significant differences across national and local news domains. Finally, we found that while it may be possible to automatically recommend similar news using feature-specific metrics, their representativeness and appropriateness varied. We explain how our findings can guide the design of future content-based and hybrid recommender strategies in the news domain.},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
In news media, recommender system technology faces several domain-specific challenges. The continuous stream of new content and users deems content-based recommendation strategies, based on similar-item retrieval, to remain popular. However, a persistent challenge is to select relevant features and corresponding similarity functions, and whether this depends on the specific context. We evaluated feature-specific similarity metrics using human similarity judgments across national and local news domains. We performed an online experiment ($N = 141$) where we asked participants to judge the similarity between pairs of randomly sampled news articles. We had three contributions: (1) comparing novel metrics based on large language models to ones traditionally used in news recommendations, (2) exploring differences in similarity judgments across national and local news domains, and (3) examining which content-based strategies were perceived as appropriate in the news domain. Our results showed that one of the novel large language model based metrics (SBERT) was highly correlated with human judgments, while there were only small, most non-significant differences across national and local news domains. Finally, we found that while it may be possible to automatically recommend similar news using feature-specific metrics, their representativeness and appropriateness varied. We explain how our findings can guide the design of future content-based and hybrid recommender strategies in the news domain.
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.
@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 = {},
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.
Jia Hua Jeng; Gloria Anne Babile Kasangu; Alain D. Starke; Christoph Trattner
Emotional Reframing of Economic News using a Large Language Model Conference
ACM UMAP 2024, 2024.
@conference{emorefram24,
title = {Emotional Reframing of Economic News using a Large Language Model},
author = {Jia Hua Jeng and Gloria Anne Babile Kasangu and Alain D. Starke and Christoph Trattner},
url = {https://mediafutures.no/umap2024___jeng_alain_gloria_christoph__workshop_-3/},
year = {2024},
date = {2024-07-01},
urldate = {2024-07-01},
booktitle = {ACM UMAP 2024},
abstract = {News media framing can shape public perception and potentially polarize views. Emotional language can exacerbate these framing effects, as a user’s emotional state can be an important contextual factor to use in news recommendation. Our research explores the relation between emotional framing techniques and the emotional states of readers, as well as readers’ perceived trust in specific news articles. Users (N = 200) had to read three economic news articles from the Washington Post. We used ChatGPT-4 to reframe news articles with specific emotional languages (Anger, Fear, Hope), compared to a neutral baseline reframed by a human journalist. Our results revealed that negative framing (Anger, Fear) elicited stronger negative emotional states among users than the neutral baseline, while Hope led to little changes overall. In contrast, perceived trust levels varied little across the different conditions. We discuss the implications of our findings and how emotional framing could affect societal polarization issues},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
News media framing can shape public perception and potentially polarize views. Emotional language can exacerbate these framing effects, as a user’s emotional state can be an important contextual factor to use in news recommendation. Our research explores the relation between emotional framing techniques and the emotional states of readers, as well as readers’ perceived trust in specific news articles. Users (N = 200) had to read three economic news articles from the Washington Post. We used ChatGPT-4 to reframe news articles with specific emotional languages (Anger, Fear, Hope), compared to a neutral baseline reframed by a human journalist. Our results revealed that negative framing (Anger, Fear) elicited stronger negative emotional states among users than the neutral baseline, while Hope led to little changes overall. In contrast, perceived trust levels varied little across the different conditions. We discuss the implications of our findings and how emotional framing could affect societal polarization issues
Anastasiia Klimashevskaia; Dietmar Jannach; Mehdi Elahi; Christoph Trattner
A Survey on Popularity Bias in Recommender Systems Journal Article
In: User Modeling and User-Adapted Interaction (UMUAI), 2024.
@article{biasanas24,
title = {A Survey on Popularity Bias in Recommender Systems},
author = {Anastasiia Klimashevskaia and Dietmar Jannach and Mehdi Elahi and Christoph Trattner},
url = {https://mediafutures.no/popularitybias_literature_review-5/},
year = {2024},
date = {2024-06-13},
urldate = {2024-06-13},
journal = {User Modeling and User-Adapted Interaction (UMUAI)},
abstract = {Recommender systems help people find relevant content in a personalized way. One main promise of such systems is that they are able to increase the visibility of items in the long tail, i.e., the lesser-
known items in a catalogue. Existing research, however, suggests that in many situations today’s recommendation algorithms instead exhibit a popularity bias, meaning that they often focus on rather popular items in their recommendations. Such a bias may not only lead to the limited value of the recommendations for consumers and providers in the short run, but it may also cause undesired reinforcement effects over time. In this paper, we discuss the potential reasons for popularity bias and review existing approaches to detect, quantify and mitigate popularity bias in
recommender systems. Our survey, therefore, includes both an overview
of the computational metrics used in the literature as well as a review of the main technical approaches to reduce the bias. Furthermore, we critically discuss today’s literature, where we observe that the research is almost entirely based on computational experiments and on certain
assumptions regarding the practical effects of including long-tail items in the recommendations.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Recommender systems help people find relevant content in a personalized way. One main promise of such systems is that they are able to increase the visibility of items in the long tail, i.e., the lesser-
known items in a catalogue. Existing research, however, suggests that in many situations today’s recommendation algorithms instead exhibit a popularity bias, meaning that they often focus on rather popular items in their recommendations. Such a bias may not only lead to the limited value of the recommendations for consumers and providers in the short run, but it may also cause undesired reinforcement effects over time. In this paper, we discuss the potential reasons for popularity bias and review existing approaches to detect, quantify and mitigate popularity bias in
recommender systems. Our survey, therefore, includes both an overview
of the computational metrics used in the literature as well as a review of the main technical approaches to reduce the bias. Furthermore, we critically discuss today’s literature, where we observe that the research is almost entirely based on computational experiments and on certain
assumptions regarding the practical effects of including long-tail items in the recommendations.
known items in a catalogue. Existing research, however, suggests that in many situations today’s recommendation algorithms instead exhibit a popularity bias, meaning that they often focus on rather popular items in their recommendations. Such a bias may not only lead to the limited value of the recommendations for consumers and providers in the short run, but it may also cause undesired reinforcement effects over time. In this paper, we discuss the potential reasons for popularity bias and review existing approaches to detect, quantify and mitigate popularity bias in
recommender systems. Our survey, therefore, includes both an overview
of the computational metrics used in the literature as well as a review of the main technical approaches to reduce the bias. Furthermore, we critically discuss today’s literature, where we observe that the research is almost entirely based on computational experiments and on certain
assumptions regarding the practical effects of including long-tail items in the recommendations.
Maria Soledad Pera; Federica Cena; Monica Landoni; Cataldo Musto; Alain D. Starke
Human Factors in User Modeling for Intelligent Systems Book Chapter
In: pp. 3–42, A Human-Centered Perspective of Intelligent Personalized Environments and Systems, 2024.
@inbook{Alain_humanF24,
title = {Human Factors in User Modeling for Intelligent Systems},
author = {Maria Soledad Pera and Federica Cena and Monica Landoni and Cataldo Musto and Alain D. Starke},
url = {https://mediafutures.no/pera2024-book-chapter-holistic-user-modeling/},
year = {2024},
date = {2024-05-01},
urldate = {2024-05-01},
pages = {3–42},
edition = {A Human-Centered Perspective of Intelligent Personalized Environments and Systems},
series = {Human–Computer Interaction Series.},
abstract = {In the current digital landscape, humans take center stage. This has caused a paradigm shift in the realm of intelligent technologies, prompting researchers and (industry) practitioners to reflect on the challenges and complexities involved in understanding the (potential) users of the technologies they develop. In this chapter, we provide an overview of human factors in user modeling, a core component of human-centered intelligent solutions. We discuss critical concepts, dimensions, and theories that inform the design of user models that are more attuned to human characteristics. Additionally, we emphasize the need for a comprehensive user model that simultaneously considers multiple factors to represent the intricacies of individuals’ interests and behaviors. Such a holistic model can, in turn, shape the design of intelligent solutions that are better able to adapt and cater to a wide range of user groups.},
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In the current digital landscape, humans take center stage. This has caused a paradigm shift in the realm of intelligent technologies, prompting researchers and (industry) practitioners to reflect on the challenges and complexities involved in understanding the (potential) users of the technologies they develop. In this chapter, we provide an overview of human factors in user modeling, a core component of human-centered intelligent solutions. We discuss critical concepts, dimensions, and theories that inform the design of user models that are more attuned to human characteristics. Additionally, we emphasize the need for a comprehensive user model that simultaneously considers multiple factors to represent the intricacies of individuals’ interests and behaviors. Such a holistic model can, in turn, shape the design of intelligent solutions that are better able to adapt and cater to a wide range of user groups.
Alain D. Starke; Martijn C. Willemsen
Psychologically Informed Design of Energy Recommender Systems: Are Nudges Still Effective in Tailored Choice Environments? Book Chapter
In: pp. 221–259, A Human-Centered Perspective of Intelligent Personalized Environments and Systems, 2024.
BibTeX | Links:
@inbook{Alain_pysch24,
title = {Psychologically Informed Design of Energy Recommender Systems: Are Nudges Still Effective in Tailored Choice Environments?},
author = {Alain D. Starke and Martijn C. Willemsen},
url = {https://mediafutures.no/starke2024-book-chapter-psych-informed-energy-recsys-4/},
year = {2024},
date = {2024-05-01},
urldate = {2024-05-01},
pages = {221–259},
edition = {A Human-Centered Perspective of Intelligent Personalized Environments and Systems},
series = {Human–Computer Interaction Series},
keywords = {},
pubstate = {published},
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}
Peder Haugfos; John Magnus Ragnhildson Dahl; Jan Kratzer; Ines Wolf
Branding or visual storytelling? How legacy media use visual journalism to reach young people in the age of digitalization Journal Article
In: Journal of Applied Journalism & Media Studies, pp. 1-24, 2024.
@article{AJMS_FT_art_Dahl,
title = {Branding or visual storytelling? How legacy media use visual journalism to reach young people in the age of digitalization},
author = {Peder Haugfos and John Magnus Ragnhildson Dahl and Jan Kratzer and Ines Wolf},
url = {https://mediafutures.no/ajms_ft_art_dahl-3/},
year = {2024},
date = {2024-01-19},
urldate = {2024-01-19},
journal = {Journal of Applied Journalism & Media Studies},
pages = {1-24},
abstract = {This article discusses how legacy media companies are responding to the real or imagined
challenge of reaching young people in the age of digitalization, by investigating two different
strategies for how to make use of the so-called Generation Z’s preference for sensory
and visual journalism. Through expert interviews, we present how the Norwegian public
broadcaster NRK P3 and the biggest newspaper in Norway, VG, approach the challenge of
catching young people’s attention. We identify two strategies for how legacy media companies
work with visual expressions and designing their digital content when trying to reach
a wide, fragmented young target group on digital platforms. VG is a platform-oriented
storyteller where design is connected to the direction and mode of storytelling and tailored
to fit certain platforms. Their area of focus is being where their target groups are and
meeting the visual expectations of these audiences by taking cues from what is trending on
different platforms. NRK P3 takes the approach of a traditional brander that works in
both visionary and traditional ways to build a strong, trustworthy and visible brand. We
conclude by pointing out possible problems with both approaches.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
This article discusses how legacy media companies are responding to the real or imagined
challenge of reaching young people in the age of digitalization, by investigating two different
strategies for how to make use of the so-called Generation Z’s preference for sensory
and visual journalism. Through expert interviews, we present how the Norwegian public
broadcaster NRK P3 and the biggest newspaper in Norway, VG, approach the challenge of
catching young people’s attention. We identify two strategies for how legacy media companies
work with visual expressions and designing their digital content when trying to reach
a wide, fragmented young target group on digital platforms. VG is a platform-oriented
storyteller where design is connected to the direction and mode of storytelling and tailored
to fit certain platforms. Their area of focus is being where their target groups are and
meeting the visual expectations of these audiences by taking cues from what is trending on
different platforms. NRK P3 takes the approach of a traditional brander that works in
both visionary and traditional ways to build a strong, trustworthy and visible brand. We
conclude by pointing out possible problems with both approaches.
challenge of reaching young people in the age of digitalization, by investigating two different
strategies for how to make use of the so-called Generation Z’s preference for sensory
and visual journalism. Through expert interviews, we present how the Norwegian public
broadcaster NRK P3 and the biggest newspaper in Norway, VG, approach the challenge of
catching young people’s attention. We identify two strategies for how legacy media companies
work with visual expressions and designing their digital content when trying to reach
a wide, fragmented young target group on digital platforms. VG is a platform-oriented
storyteller where design is connected to the direction and mode of storytelling and tailored
to fit certain platforms. Their area of focus is being where their target groups are and
meeting the visual expectations of these audiences by taking cues from what is trending on
different platforms. NRK P3 takes the approach of a traditional brander that works in
both visionary and traditional ways to build a strong, trustworthy and visible brand. We
conclude by pointing out possible problems with both approaches.
2023
Erik Knudsen
Modeling news recommender systems’ conditional effects on selective exposure: evidence from two online experiments Best Paper Journal Article
In: Journal of Communication , 2023.
@article{nokey,
title = {Modeling news recommender systems’ conditional effects on selective exposure: evidence from two online experiments},
author = {Erik Knudsen},
url = {https://mediafutures.no/jqac047/},
year = {2023},
date = {2023-12-23},
urldate = {2023-12-23},
journal = {Journal of Communication },
abstract = {Under which conditions do news recommender systems (NRSs) amplify or reduce selective exposure? I provide the Recommender Influenced Selective Exposure framework, which aims to enable researchers to model and study the conditional effects of NRSs on selective exposure. I empirically test this framework by studying user behavior on a news site where the choice environment is designed to systematically influence selective exposure. Through two preregistered online experiments that simulate different NRSs and unobtrusively log user behavior, I contribute empirical evidence that an NRS can increase or decrease the chance that selective exposure occurs, depending on what the NRS is designed to achieve. These insights have implications for ongoing scholarly debates on the democratic impact of NRSs.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Under which conditions do news recommender systems (NRSs) amplify or reduce selective exposure? I provide the Recommender Influenced Selective Exposure framework, which aims to enable researchers to model and study the conditional effects of NRSs on selective exposure. I empirically test this framework by studying user behavior on a news site where the choice environment is designed to systematically influence selective exposure. Through two preregistered online experiments that simulate different NRSs and unobtrusively log user behavior, I contribute empirical evidence that an NRS can increase or decrease the chance that selective exposure occurs, depending on what the NRS is designed to achieve. These insights have implications for ongoing scholarly debates on the democratic impact of NRSs.
Erik Knudsen; Alain D. Starke; Christoph Trattner
Topical Preference Trumps Other Features in News Recommendation: A Conjoint Analysis on a Representative Sample from Norway Conference
Association for Computing Machinery (ACM) RecSys ’23, 2023.
BibTeX | Links:
@conference{inra2023-1,
title = {Topical Preference Trumps Other Features in News Recommendation: A Conjoint Analysis on a Representative Sample from Norway},
author = {Erik Knudsen and Alain D. Starke and Christoph Trattner },
url = {https://mediafutures.no/inra2023-1/},
year = {2023},
date = {2023-09-18},
urldate = {2023-09-18},
booktitle = {Association for Computing Machinery (ACM) RecSys ’23},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
Khadiga Seddik; Erik Knudsen; Damian Trilling; Christoph Trattner
Understanding How News Recommender Systems Influence Selective Exposure Conference
Association for Computing Machinery (ACM) RecSys ’23, 2023.
BibTeX | Links:
@conference{behavrec2023,
title = {Understanding How News Recommender Systems Influence Selective Exposure},
author = {Khadiga Seddik and Erik Knudsen and Damian Trilling and Christoph Trattner },
url = {https://mediafutures.no/behavrec2023/},
year = {2023},
date = {2023-09-18},
urldate = {2023-09-18},
booktitle = {Association for Computing Machinery (ACM) RecSys ’23},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
Anastasiia Klimashevskaia; Mehdi Elahi; Dietmar Jannach; Lars Skjærven; Astrid Tessem; Christoph Trattner
Evaluating The Effects of Calibrated Popularity Bias Mitigation: A Field Study Conference
Association for Computing Machinery (ACM) RecSys ’23, 2023.
BibTeX | Links:
@conference{RecSys_2023_LBR,
title = {Evaluating The Effects of Calibrated Popularity Bias Mitigation: A Field Study},
author = {Anastasiia Klimashevskaia and Mehdi Elahi and Dietmar Jannach and Lars Skjærven and Astrid Tessem and Christoph Trattner},
url = {https://mediafutures.no/recsys_2023_lbr/},
year = {2023},
date = {2023-09-18},
booktitle = {Association for Computing Machinery (ACM) RecSys ’23},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
Ayoub El Majjodi; Alain D. Starke; Christoph Trattner
The Interplay between Food Knowledge, Nudges, and Preference Elicitation Methods Determines the Evaluation of a Recipe Recommender System Conference
Association for Computing Machinery (ACM) RecSys ’23, 2023.
BibTeX | Links:
@conference{inra2023,
title = {The Interplay between Food Knowledge, Nudges, and Preference Elicitation Methods Determines the Evaluation of a Recipe Recommender System},
author = {Ayoub El Majjodi and Alain D. Starke and Christoph Trattner },
url = {https://mediafutures.no/intrs2023-2/},
year = {2023},
date = {2023-09-18},
urldate = {2023-09-18},
booktitle = {Association for Computing Machinery (ACM) RecSys ’23},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
Alain Starke; Kimia Emami; Andrea Makarová; Bruce Ferwerda
Using Visual and Linguistic Framing to Support Sustainable Decisions in an Online Store Conference
Association for Computing Machinery (ACM) RecSys ’23,, 2023.
BibTeX | Links:
@conference{intrs23_session3,
title = {Using Visual and Linguistic Framing to Support Sustainable Decisions in an Online Store},
author = {Alain Starke and Kimia Emami and Andrea Makarová and Bruce Ferwerda },
url = {https://mediafutures.no/intrs23_session3_paper_2/},
year = {2023},
date = {2023-09-18},
urldate = {2023-09-18},
booktitle = {Association for Computing Machinery (ACM) RecSys ’23,},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
Jia Hua Jeng; Alain D. Starke; Christoph Trattner
Towards Attitudinal Change in News Recommender Systems: A Pilot Study on Climate Change Workshop
2023.
@workshop{Jeng2023,
title = {Towards Attitudinal Change in News Recommender Systems: A Pilot Study on Climate Change},
author = {Jia Hua Jeng and Alain D. Starke and Christoph Trattner},
url = {https://mediafutures.no/jeng2023-towards-attitudinal-change-in-news2908-2/},
year = {2023},
date = {2023-04-18},
urldate = {2023-04-18},
abstract = {Personalized recommender systems facilitate decision-making in various domains by presenting content closely aligned with users’ preferences.
However, personalization can lead to unintended consequences. In news, selective information exposure and consumption might amplify
polarization, as users are empowered to seek out information that is in line with their own attitudes and viewpoints. However, personalization in
terms of algorithmic content and persuasive technology could also help to narrow the gap between polarized user attitudes and news consumption
patterns. This paper presents a pilot study on climate change news. We examined the relation between users’ level of environmental concern, their preferences
for news articles, and news article content. We aimed to capture a news article’s viewpoint through sentiment analysis. Users (N = 180)
were asked to read and evaluate 10 news articles from the Washington Post. We found a positive correlation between users’ level of environmental
concern and whether they liked the article. In contrast, no significant correlation was found between sentiment and environmental concern.
We argue why a different type of news article analysis than sentiment is needed. Finally, we present our research agenda on how persuasive technology
might help to support more exploration of news article viewpoints in the future.},
keywords = {},
pubstate = {published},
tppubtype = {workshop}
}
Personalized recommender systems facilitate decision-making in various domains by presenting content closely aligned with users’ preferences.
However, personalization can lead to unintended consequences. In news, selective information exposure and consumption might amplify
polarization, as users are empowered to seek out information that is in line with their own attitudes and viewpoints. However, personalization in
terms of algorithmic content and persuasive technology could also help to narrow the gap between polarized user attitudes and news consumption
patterns. This paper presents a pilot study on climate change news. We examined the relation between users’ level of environmental concern, their preferences
for news articles, and news article content. We aimed to capture a news article’s viewpoint through sentiment analysis. Users (N = 180)
were asked to read and evaluate 10 news articles from the Washington Post. We found a positive correlation between users’ level of environmental
concern and whether they liked the article. In contrast, no significant correlation was found between sentiment and environmental concern.
We argue why a different type of news article analysis than sentiment is needed. Finally, we present our research agenda on how persuasive technology
might help to support more exploration of news article viewpoints in the future.
However, personalization can lead to unintended consequences. In news, selective information exposure and consumption might amplify
polarization, as users are empowered to seek out information that is in line with their own attitudes and viewpoints. However, personalization in
terms of algorithmic content and persuasive technology could also help to narrow the gap between polarized user attitudes and news consumption
patterns. This paper presents a pilot study on climate change news. We examined the relation between users’ level of environmental concern, their preferences
for news articles, and news article content. We aimed to capture a news article’s viewpoint through sentiment analysis. Users (N = 180)
were asked to read and evaluate 10 news articles from the Washington Post. We found a positive correlation between users’ level of environmental
concern and whether they liked the article. In contrast, no significant correlation was found between sentiment and environmental concern.
We argue why a different type of news article analysis than sentiment is needed. Finally, we present our research agenda on how persuasive technology
might help to support more exploration of news article viewpoints in the future.
Marianne Borchgrevink-Brækhus; Hallvard Moe
The Burden of Subscribing: How Young People Experience Digital News Subscriptions Journal Article
In: Journalism Studies , 2023.
@article{Borchgrevink-Brækhus2023,
title = {The Burden of Subscribing: How Young People Experience Digital News Subscriptions},
author = {Marianne Borchgrevink-Brækhus and Hallvard Moe },
url = {https://mediafutures.no/the-burden-of-subscribing-how-young-people-experience-digital-news-subscriptions/},
year = {2023},
date = {2023-04-17},
urldate = {2023-04-17},
journal = {Journalism Studies },
abstract = {This paper analyzes how young non-paying news users experience digital news subscriptions in Norway. As news organizations face
declining advertising revenues, digital subscriptions are considered the sustainable financial strategy of the future, with young people a particularly challenging group to convert. We analyze the experiences of young adults who do not pay for news and identify three key dimensions to why they do not subscribe:
lack of exclusivity, subscriptions as too time-consuming, and unattractive payment models. We also detail how the informants maneuver around paywalls, and we highlight “multiperspectivism” as an overarching concern guiding the informants’ preferences. Empirically, the paper furthers our understanding of
the challenges facing business models for journalism, especially problems with long-term, provider-specific subscriptions. Methodologically, we demonstrate how a combination of recurring interviews and a media diary matching a subscription test period yields a deeper analysis of motivations for, and
experiences with, news use. Theoretically, the paper shows how approaching news through users’ experiences can provide insights not just into what users appreciate from news but also into where they consider there is a lack of value.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
This paper analyzes how young non-paying news users experience digital news subscriptions in Norway. As news organizations face
declining advertising revenues, digital subscriptions are considered the sustainable financial strategy of the future, with young people a particularly challenging group to convert. We analyze the experiences of young adults who do not pay for news and identify three key dimensions to why they do not subscribe:
lack of exclusivity, subscriptions as too time-consuming, and unattractive payment models. We also detail how the informants maneuver around paywalls, and we highlight “multiperspectivism” as an overarching concern guiding the informants’ preferences. Empirically, the paper furthers our understanding of
the challenges facing business models for journalism, especially problems with long-term, provider-specific subscriptions. Methodologically, we demonstrate how a combination of recurring interviews and a media diary matching a subscription test period yields a deeper analysis of motivations for, and
experiences with, news use. Theoretically, the paper shows how approaching news through users’ experiences can provide insights not just into what users appreciate from news but also into where they consider there is a lack of value.
declining advertising revenues, digital subscriptions are considered the sustainable financial strategy of the future, with young people a particularly challenging group to convert. We analyze the experiences of young adults who do not pay for news and identify three key dimensions to why they do not subscribe:
lack of exclusivity, subscriptions as too time-consuming, and unattractive payment models. We also detail how the informants maneuver around paywalls, and we highlight “multiperspectivism” as an overarching concern guiding the informants’ preferences. Empirically, the paper furthers our understanding of
the challenges facing business models for journalism, especially problems with long-term, provider-specific subscriptions. Methodologically, we demonstrate how a combination of recurring interviews and a media diary matching a subscription test period yields a deeper analysis of motivations for, and
experiences with, news use. Theoretically, the paper shows how approaching news through users’ experiences can provide insights not just into what users appreciate from news but also into where they consider there is a lack of value.
Erik Knudsen; Åsta Dyrnes Nordø; Magnus Hoem Iversen
How Rally-Round-the-Flag Effects Shape Trust in the News Media: Evidence from Panel Waves before and during the COVID-19 Pandemic Crisis Journal Article
In: Political Communication, 2023.
@article{Knudsen2023,
title = {How Rally-Round-the-Flag Effects Shape Trust in the News Media: Evidence from Panel Waves before and during the COVID-19 Pandemic Crisis},
author = {Erik Knudsen and Åsta Dyrnes Nordø and Magnus Hoem Iversen},
url = {https://mediafutures.no/how-rally-round-the-flag-effects-shape-trust-in-the-news-media-evidence-from-panel-waves-before-and-during-the-covid-19-pandemic-crisis/},
year = {2023},
date = {2023-02-23},
urldate = {2023-02-23},
journal = {Political Communication},
abstract = {In this study, we extend the literature on the rally ‘round the flag phenomenon, that is, that international crises tend to cause an increase in citizens’ approval of political institutions. We advance this literature and highlight its relevance for political communication research in three ways: 1) by theorizing and empirically testing two arguments for why rally effects should extend to trust in the news media on the institutional level, 2) by providing empirical evidence on how rally effects on trust in the media develop over time during an international crisis, and 3) by theorizing and testing the conditions under which rally effects on media trust are more likely to occur by studying heterogeneous effects. Through a panel design with a pre-crisis baseline of Norwegian citizens’ trust in news media, we find evidence to suggest that the compound effect of the COVID-19 pandemic crisis caused a long-lasting increase in trust in the news media in Norway, and that the degree of increase varied by citizens’ education and whether they belonged to a “high-risk” group. We also provide evidence to suggest that rally effects on news media trust are contingent on how important the news media is as a source of information about the crisis and the “trust nexus” between media trust and political trust. These insights extend our current understanding of how times of crisis affect trust in the news media.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
In this study, we extend the literature on the rally ‘round the flag phenomenon, that is, that international crises tend to cause an increase in citizens’ approval of political institutions. We advance this literature and highlight its relevance for political communication research in three ways: 1) by theorizing and empirically testing two arguments for why rally effects should extend to trust in the news media on the institutional level, 2) by providing empirical evidence on how rally effects on trust in the media develop over time during an international crisis, and 3) by theorizing and testing the conditions under which rally effects on media trust are more likely to occur by studying heterogeneous effects. Through a panel design with a pre-crisis baseline of Norwegian citizens’ trust in news media, we find evidence to suggest that the compound effect of the COVID-19 pandemic crisis caused a long-lasting increase in trust in the news media in Norway, and that the degree of increase varied by citizens’ education and whether they belonged to a “high-risk” group. We also provide evidence to suggest that rally effects on news media trust are contingent on how important the news media is as a source of information about the crisis and the “trust nexus” between media trust and political trust. These insights extend our current understanding of how times of crisis affect trust in the news media.
John Magnus Ragnhildson Dahl; Brita Ytre-Arne
Monitoring the infection rate: Explaining the meaning of metrics in pandemic news experiences Journal Article
In: Journalism , 2023.
@article{nokey,
title = {Monitoring the infection rate: Explaining the meaning of metrics in pandemic news experiences},
author = {John Magnus Ragnhildson Dahl and Brita Ytre-Arne},
url = {https://mediafutures.no/john-magnus-and-brita/},
year = {2023},
date = {2023-01-03},
journal = {Journalism },
abstract = {The COVID-19 pandemic has brought forward questions of what citizens need and want from journalism in a global crisis. In this article, we analyse one particular aspect of pandemic news experiences: Preoccupation with monitoring metrics for COVID-19 infection cases, hospitalisations, and deaths, widely disseminated through journalistic news outlets. We ask why close monitoring of such metrics appeared meaningful to news users, and what these experiences can tell us about the role of journalism in the pandemic information environment. Our analysis draws on qualitative research conducted in Norway in 2020, finding users particularly devoted to monitoring metrics, both in early lockdown and during the second wave of COVID-19. To contextualize our findings, we draw on scholarship on emotional responses to data in the everyday, and on the social role of journalism. We argue that monitoring of infection rates is an expression of trust in the media as a provider of factual information, also expressed by those who are cynical towards other aspects of journalism, and we conceptualise this monitoring practice as a coping strategy to deal with the pandemic as an unknown and uncontrollable threat, involving difficult emotions of uncertainty and fear.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The COVID-19 pandemic has brought forward questions of what citizens need and want from journalism in a global crisis. In this article, we analyse one particular aspect of pandemic news experiences: Preoccupation with monitoring metrics for COVID-19 infection cases, hospitalisations, and deaths, widely disseminated through journalistic news outlets. We ask why close monitoring of such metrics appeared meaningful to news users, and what these experiences can tell us about the role of journalism in the pandemic information environment. Our analysis draws on qualitative research conducted in Norway in 2020, finding users particularly devoted to monitoring metrics, both in early lockdown and during the second wave of COVID-19. To contextualize our findings, we draw on scholarship on emotional responses to data in the everyday, and on the social role of journalism. We argue that monitoring of infection rates is an expression of trust in the media as a provider of factual information, also expressed by those who are cynical towards other aspects of journalism, and we conceptualise this monitoring practice as a coping strategy to deal with the pandemic as an unknown and uncontrollable threat, involving difficult emotions of uncertainty and fear.
2022
Ayoub El Majjodi; Alain D. Starke; Christoph Trattner
Nudging Towards Health? Examining the Merits of Nutrition Labels and Personalization in a Recipe Recommender System Conference
Nudging Towards Health? Examining the Merits of Nutrition Labels and Personalization in a Recipe Recommender System, 2022.
@conference{Majjodi2022,
title = {Nudging Towards Health? Examining the Merits of Nutrition Labels and Personalization in a Recipe Recommender System},
author = { Ayoub El Majjodi and Alain D. Starke and Christoph Trattner
},
url = {https://dl.acm.org/doi/10.1145/3503252.3531312?fbclid=IwAR0eb6MPuISpVs9Vfkd-ww_KN7EjbMbiGdDQnPxjayogfKbHFgkSgeLdaxs},
year = {2022},
date = {2022-07-03},
urldate = {2022-07-03},
booktitle = {Nudging Towards Health? Examining the Merits of Nutrition Labels and Personalization in a Recipe Recommender System},
abstract = {Food recommender systems show personalized recipes to users based on content liked previously. Despite their potential, often recommended (popular) recipes in previous studies have turned out to be unhealthy, negatively contributing to prevalent obesity problems worldwide. Changing how foods are presented through digital nudges might help, but these are usually examined in non-personalized contexts, such as a brick-and-mortar supermarket. This study seeks to support healthy food choices in a personalized interface by adding front-of-package nutrition labels to recipes in a food recommender system. After performing an offline evaluation, we conducted an online study (N = 600) with six different recommender interfaces, based on a 2 (non-personalized vs. personalized recipe advice) x 3 (No Label, Multiple Traffic Light, Nutri-Score) between-subjects design. We found that recipe choices made in the non-personalized scenario were healthier, while the use of nutrition labels (our digital nudge) reduced choice difficulty when the content was personalized.},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
Food recommender systems show personalized recipes to users based on content liked previously. Despite their potential, often recommended (popular) recipes in previous studies have turned out to be unhealthy, negatively contributing to prevalent obesity problems worldwide. Changing how foods are presented through digital nudges might help, but these are usually examined in non-personalized contexts, such as a brick-and-mortar supermarket. This study seeks to support healthy food choices in a personalized interface by adding front-of-package nutrition labels to recipes in a food recommender system. After performing an offline evaluation, we conducted an online study (N = 600) with six different recommender interfaces, based on a 2 (non-personalized vs. personalized recipe advice) x 3 (No Label, Multiple Traffic Light, Nutri-Score) between-subjects design. We found that recipe choices made in the non-personalized scenario were healthier, while the use of nutrition labels (our digital nudge) reduced choice difficulty when the content was personalized.
Mehdi Elahi; Alain D. Starke; Nabil El Ioini; Anna Alexander Lambrix; Christoph Trattner
Developing and Evaluating a University Recommender System Journal Article
In: Frontiers in Artificial Intelligence , 2022.
@article{Elahi2022,
title = {Developing and Evaluating a University Recommender System},
author = {Mehdi Elahi and Alain D. Starke and Nabil El Ioini and Anna Alexander Lambrix and Christoph Trattner},
url = {https://www.frontiersin.org/articles/10.3389/frai.2021.796268/full},
doi = {https://doi.org/10.3389/frai.2021.796268},
year = {2022},
date = {2022-02-02},
journal = {Frontiers in Artificial Intelligence },
abstract = {A challenge for many young adults is to find the right institution to follow higher education. Global university rankings are a commonly used, but inefficient tool, for they do not consider a person's preferences and needs. For example, some persons pursue prestige in their higher education, while others prefer proximity. This paper develops and evaluates a university recommender system, eliciting user preferences as ratings to build predictive models and to generate personalized university ranking lists. In Study 1, we performed offline evaluation on a rating dataset to determine which recommender approaches had the highest predictive value. In Study 2, we selected three algorithms to produce different university recommendation lists in our online tool, asking our users to compare and evaluate them in terms of different metrics (Accuracy, Diversity, Perceived Personalization, Satisfaction, and Novelty). We show that a SVD algorithm scores high on accuracy and perceived personalization, while a KNN algorithm scores better on novelty. We also report findings on preferred university features.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
A challenge for many young adults is to find the right institution to follow higher education. Global university rankings are a commonly used, but inefficient tool, for they do not consider a person's preferences and needs. For example, some persons pursue prestige in their higher education, while others prefer proximity. This paper develops and evaluates a university recommender system, eliciting user preferences as ratings to build predictive models and to generate personalized university ranking lists. In Study 1, we performed offline evaluation on a rating dataset to determine which recommender approaches had the highest predictive value. In Study 2, we selected three algorithms to produce different university recommendation lists in our online tool, asking our users to compare and evaluate them in terms of different metrics (Accuracy, Diversity, Perceived Personalization, Satisfaction, and Novelty). We show that a SVD algorithm scores high on accuracy and perceived personalization, while a KNN algorithm scores better on novelty. We also report findings on preferred university features.
2021
Alain D. Starke; Martijn C. Willemsen; Christoph Trattner
Nudging Healthy Choices in Food Search Through Visual Attractiveness Journal Article
In: no. April 2021, pp. 1-18, 2021.
@article{Starke2021,
title = {Nudging Healthy Choices in Food Search Through Visual Attractiveness},
author = {Alain D. Starke and Martijn C. Willemsen and Christoph Trattner},
url = {https://www.frontiersin.org/articles/10.3389/frai.2021.621743/full},
doi = {10.3389/frai.2021.621743},
year = {2021},
date = {2021-04-22},
number = {April 2021},
pages = {1-18},
abstract = {Recipe websites are becoming increasingly popular to support people in their home cooking. However, most of these websites prioritize popular recipes, which tend to be unhealthy. Drawing upon research on visual biases and nudges, this paper investigates whether healthy food choices can be supported in food search by depicting attractive images alongside recipes, as well as by re-ranking search results on health. After modelling the visual attractiveness of recipe images, we asked 239 users to search for specific online recipes and to select those they liked the most. Our analyses revealed that users tended to choose a healthier recipe if a visually attractive image was depicted alongside it, as well as if it was listed at the top of a list of search results. Even though less popular recipes were promoted this way, it did not come at the cost of a user’s level of satisfaction},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Recipe websites are becoming increasingly popular to support people in their home cooking. However, most of these websites prioritize popular recipes, which tend to be unhealthy. Drawing upon research on visual biases and nudges, this paper investigates whether healthy food choices can be supported in food search by depicting attractive images alongside recipes, as well as by re-ranking search results on health. After modelling the visual attractiveness of recipe images, we asked 239 users to search for specific online recipes and to select those they liked the most. Our analyses revealed that users tended to choose a healthier recipe if a visually attractive image was depicted alongside it, as well as if it was listed at the top of a list of search results. Even though less popular recipes were promoted this way, it did not come at the cost of a user’s level of satisfaction
Jelena Kleut; Ana Milojevic
Framing Protest in Online News and Readers’ Comments: The Case of Serbian Protest “Against Dictatorship” Journal Article
In: International Journal of Communication, vol. 15, no. 21, pp. 82-102, 2021, (Pre SFI).
@article{Kleut2021,
title = {Framing Protest in Online News and Readers’ Comments: The Case of Serbian Protest “Against Dictatorship”},
author = {Jelena Kleut and Ana Milojevic},
url = {https://www.researchgate.net/publication/348787747_Framing_Protest_in_Online_News_and_Readers\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'_Comments_The_Case_of_Serbian_Protest_Against_Dictatorship},
year = {2021},
date = {2021-01-01},
journal = {International Journal of Communication},
volume = {15},
number = {21},
pages = {82-102},
series = {Not connected to SFI MediaFutures},
abstract = {This research examines the "protest paradigm" in the digital news environment of a politically polarized media system by considering relations between news and online readers' comments about the Serbian protest Against Dictatorship, which was held in 2017. Applying content analysis to news and comments from two news websites, our study indicates the need to account for opposing framing of the protest (violence/peacefulness, de/legitimizing and un/democratic) in a polarized environment. The results show that the distribution of opposing frames is guided by the media relations with the government. Online readers' comments generally enhance this polarized pattern of frame distribution, with the exception of the performance frame, which remains prolific in the media, but absent from readers' comments.},
note = {Pre SFI},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
This research examines the "protest paradigm" in the digital news environment of a politically polarized media system by considering relations between news and online readers' comments about the Serbian protest Against Dictatorship, which was held in 2017. Applying content analysis to news and comments from two news websites, our study indicates the need to account for opposing framing of the protest (violence/peacefulness, de/legitimizing and un/democratic) in a polarized environment. The results show that the distribution of opposing frames is guided by the media relations with the government. Online readers' comments generally enhance this polarized pattern of frame distribution, with the exception of the performance frame, which remains prolific in the media, but absent from readers' comments.
2020
Brita Ytre-Arne; Hallvard Moe
Folk theories of algorithms: Understanding digital irritation Journal Article
In: Media, Culture & Society, 2020, (Pre SFI).
@article{Arne2020,
title = {Folk theories of algorithms: Understanding digital irritation},
author = {Brita Ytre-Arne and Hallvard Moe},
year = {2020},
date = {2020-12-31},
journal = {Media, Culture & Society},
series = {TEST},
note = {Pre SFI},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Irene Costera Meijer; Tim Groot Kormelink
Changing news use. Unchanged news experiences? Book
Routledge, 2020, ISBN: 9780367485788, (Pre SFI).
@book{Meijer2020c,
title = {Changing news use. Unchanged news experiences?},
author = {Irene Costera Meijer and Tim Groot Kormelink},
url = {https://www.routledge.com/Changing-News-Use-Unchanged-News-Experiences/Meijer-Kormelink/p/book/9780367485788},
isbn = {9780367485788},
year = {2020},
date = {2020-11-09},
publisher = {Routledge},
abstract = {Changing News Use pulls from empirical research to introduce and describe
how changing news user patterns and journalism practices have been
mutually disruptive, exploring what journalists and the news media can
learn from these changes.
Based on 15 years of audience research, the authors provide an in-depth
description of what people do with news and how this has diversified
over time, from reading, watching, and listening to a broader spectrum
of user practices including checking, scrolling, tagging, and avoiding.
By emphasizing people’s own experience of journalism, this book also
investigates what two prominent audience measurements – clicking and
spending time – mean from a user perspective. The book outlines ways to
overcome the dilemma of providing what people apparently want (attentiongrabbing
news features) and delivering what people apparently need (what
journalists see as important information), suggesting alternative ways to
investigate and become sensitive to the practices, preferences, and pleasures
of audiences and discussing what these research findings might mean for
everyday journalism practice.
The book is a valuable and timely resource for academics and researchers
interested in the fields of journalism studies, sociology, digital media, and
communication.},
note = {Pre SFI},
keywords = {},
pubstate = {published},
tppubtype = {book}
}
Changing News Use pulls from empirical research to introduce and describe
how changing news user patterns and journalism practices have been
mutually disruptive, exploring what journalists and the news media can
learn from these changes.
Based on 15 years of audience research, the authors provide an in-depth
description of what people do with news and how this has diversified
over time, from reading, watching, and listening to a broader spectrum
of user practices including checking, scrolling, tagging, and avoiding.
By emphasizing people’s own experience of journalism, this book also
investigates what two prominent audience measurements – clicking and
spending time – mean from a user perspective. The book outlines ways to
overcome the dilemma of providing what people apparently want (attentiongrabbing
news features) and delivering what people apparently need (what
journalists see as important information), suggesting alternative ways to
investigate and become sensitive to the practices, preferences, and pleasures
of audiences and discussing what these research findings might mean for
everyday journalism practice.
The book is a valuable and timely resource for academics and researchers
interested in the fields of journalism studies, sociology, digital media, and
communication.
how changing news user patterns and journalism practices have been
mutually disruptive, exploring what journalists and the news media can
learn from these changes.
Based on 15 years of audience research, the authors provide an in-depth
description of what people do with news and how this has diversified
over time, from reading, watching, and listening to a broader spectrum
of user practices including checking, scrolling, tagging, and avoiding.
By emphasizing people’s own experience of journalism, this book also
investigates what two prominent audience measurements – clicking and
spending time – mean from a user perspective. The book outlines ways to
overcome the dilemma of providing what people apparently want (attentiongrabbing
news features) and delivering what people apparently need (what
journalists see as important information), suggesting alternative ways to
investigate and become sensitive to the practices, preferences, and pleasures
of audiences and discussing what these research findings might mean for
everyday journalism practice.
The book is a valuable and timely resource for academics and researchers
interested in the fields of journalism studies, sociology, digital media, and
communication.
Ulle Endriss; Ronald de Haan; Jerôme Lang; Marija Slavkovik
The complexity landscape of outcome determination in judgment aggregation Journal Article
In: Journal of Artificial Intelligence Research, vol. 69, pp. 687–731, 2020, (Pre SFI).
@article{Endriss2020,
title = {The complexity landscape of outcome determination in judgment aggregation},
author = {Ulle Endriss and Ronald de Haan and Jerôme Lang and Marija Slavkovik },
url = {https://www.jair.org/index.php/jair/article/view/11970/26619},
doi = {10.1613/jair.1.11970},
year = {2020},
date = {2020-11-04},
journal = {Journal of Artificial Intelligence Research},
volume = {69},
pages = {687–731},
abstract = {We provide a comprehensive analysis of the computational complexity of the outcome determinationproblem for the most important aggregation rules proposed in the literature on logic-based judgmentaggregation. Judgment aggregation is a powerful and flexible framework for studying problems ofcollective decision making that has attracted interest in a range of disciplines, including Legal Theory,Philosophy, Economics, Political Science, and Artificial Intelligence. The problem of computing theoutcome for a given list of individual judgments to be aggregated into a single collective judgment isthe most fundamental algorithmic challenge arising in this context. Our analysis applies to severaldifferent variants of the basic framework of judgment aggregation that have been discussed in theliterature, as well as to a new framework that encompasses all existing such frameworks in terms ofexpressive power and representational succinctness.},
note = {Pre SFI},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
We provide a comprehensive analysis of the computational complexity of the outcome determinationproblem for the most important aggregation rules proposed in the literature on logic-based judgmentaggregation. Judgment aggregation is a powerful and flexible framework for studying problems ofcollective decision making that has attracted interest in a range of disciplines, including Legal Theory,Philosophy, Economics, Political Science, and Artificial Intelligence. The problem of computing theoutcome for a given list of individual judgments to be aggregated into a single collective judgment isthe most fundamental algorithmic challenge arising in this context. Our analysis applies to severaldifferent variants of the basic framework of judgment aggregation that have been discussed in theliterature, as well as to a new framework that encompasses all existing such frameworks in terms ofexpressive power and representational succinctness.
Hallvard Moe; Jan Fredrik Hovden; Kari Karppinen
Operationalizing exposure diversity. Journal Article
In: European Journal of Communication, pp. 1-2, 2020, (Pre SFI).
@article{Moe2020,
title = {Operationalizing exposure diversity.},
author = {Hallvard Moe and Jan Fredrik Hovden and Kari Karppinen},
url = {https://journals.sagepub.com/doi/pdf/10.1177/0267323120966849},
doi = {10.1177/0267323120966849},
year = {2020},
date = {2020-10-29},
journal = {European Journal of Communication},
pages = {1-2},
abstract = {The concept of exposure diversity, the diversity of information that people actually access and use, has recently gained prominence in media policy debates. This aspect of media diversity, however, remains difficult to define, measure or implement in actual policy. In this article, we propose an empirical approach that operationalizes exposure diversity in terms of news and current affairs providers in the media repertoire of different social groups. This can be studied through cluster analysis of survey data on respondents’ combinations of use of different media providers and outlets. The article first discusses exposure diversity as a media policy aim. We then outline our proposal on how to take the debate a step further through empirical analysis of media repertoires, with an illustration of how such an analysis may be conducted using survey data from Norway.},
note = {Pre SFI},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The concept of exposure diversity, the diversity of information that people actually access and use, has recently gained prominence in media policy debates. This aspect of media diversity, however, remains difficult to define, measure or implement in actual policy. In this article, we propose an empirical approach that operationalizes exposure diversity in terms of news and current affairs providers in the media repertoire of different social groups. This can be studied through cluster analysis of survey data on respondents’ combinations of use of different media providers and outlets. The article first discusses exposure diversity as a media policy aim. We then outline our proposal on how to take the debate a step further through empirical analysis of media repertoires, with an illustration of how such an analysis may be conducted using survey data from Norway.
Naieme Hazrati; Mehdi Elahi
Addressing the New Item problem in video recommender systems by incorporation of visual features with restricted Boltzmann machines. Journal Article
In: Expert Systems, vol. e12645, pp. 1-20, 2020, (Pre SFI).
@article{Hazrati2020,
title = { Addressing the New Item problem in video recommender systems by incorporation of visual features with restricted Boltzmann machines.},
author = {Naieme Hazrati and Mehdi Elahi},
url = {https://onlinelibrary.wiley.com/doi/epdf/10.1111/exsy.12645},
doi = {https://doi.org/10.1111/exsy.12645},
year = {2020},
date = {2020-10-19},
journal = {Expert Systems},
volume = {e12645},
pages = {1-20},
abstract = {Over the past years, the research of video recommender systems (RSs) has been mainly focussed on the development of novel algorithms. Although beneficial, still any algorithm may fail to recommend video items that the system has no form of data associated to them (New Item Cold Start). This problem occurs when a new item is added to the catalogue of the system and no data are available for that item. In content‐based RSs, the video items are typically represented by semantic attributes, when generating recommendations. These attributes require a group of experts or users for annotation, and still, the generated recommendations might not capture a complete picture of the users' preferences, for example, the visual tastes of users on video style. This article addresses this problem by proposing recommendation based on novel visual features that do not require human annotation and can represent visual aspects of video items. We have designed a novel evaluation methodology considering three realistic scenarios, that is, (a) extreme cold start, (b) moderate cold start and (c) warm‐start scenario. We have conducted a set of comprehensive experiments, and our results have shown the superior performance of recommendations based on visual features, in all of the evaluation scenarios.},
note = {Pre SFI},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Over the past years, the research of video recommender systems (RSs) has been mainly focussed on the development of novel algorithms. Although beneficial, still any algorithm may fail to recommend video items that the system has no form of data associated to them (New Item Cold Start). This problem occurs when a new item is added to the catalogue of the system and no data are available for that item. In content‐based RSs, the video items are typically represented by semantic attributes, when generating recommendations. These attributes require a group of experts or users for annotation, and still, the generated recommendations might not capture a complete picture of the users' preferences, for example, the visual tastes of users on video style. This article addresses this problem by proposing recommendation based on novel visual features that do not require human annotation and can represent visual aspects of video items. We have designed a novel evaluation methodology considering three realistic scenarios, that is, (a) extreme cold start, (b) moderate cold start and (c) warm‐start scenario. We have conducted a set of comprehensive experiments, and our results have shown the superior performance of recommendations based on visual features, in all of the evaluation scenarios.
Brita Ytre-Arne; Trine Syvertsen; Hallvard Moe; Faltin Karlsen
Temporal ambivalences in smartphone use: Conflicting flows, conflicting responsibilities. Journal Article
In: New Media and Society, vol. 22, no. 9, pp. 1715–1732, 2020, (Pre SFI).
@article{Arne2020b,
title = {Temporal ambivalences in smartphone use: Conflicting flows, conflicting responsibilities.},
author = {Brita Ytre-Arne and Trine Syvertsen and Hallvard Moe and Faltin Karlsen },
url = {https://journals.sagepub.com/doi/pdf/10.1177/1461444820913561},
doi = {10.1177/1461444820913561},
year = {2020},
date = {2020-09-03},
journal = {New Media and Society},
volume = {22},
number = {9},
pages = {1715–1732},
abstract = {This article explores implications of the central position of the smartphone in an age of constant connectivity. Based on a qualitative study of 50 informants, we ask how users experience and handle temporal ambivalences in everyday smartphone use, drawing on the concepts flow and responsibilization to conceptualize central dimensions of such ambivalences. The notion of conflicting flows illuminates how brief checking cycles expand at the expense of other activities, resulting in a temporal conflict experienced by users. Responsibilization points to how users take individual responsibility for managing such conflicting flows, and to how this practice is difficult and conflict-ridden. We conclude that while individual time management is often framed as the solution to temporal conflicts, such attempts at regulating smartphone use appear inadequate. Our conceptualization of temporal ambivalence offers a more nuanced understanding of why this is the case.},
note = {Pre SFI},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
This article explores implications of the central position of the smartphone in an age of constant connectivity. Based on a qualitative study of 50 informants, we ask how users experience and handle temporal ambivalences in everyday smartphone use, drawing on the concepts flow and responsibilization to conceptualize central dimensions of such ambivalences. The notion of conflicting flows illuminates how brief checking cycles expand at the expense of other activities, resulting in a temporal conflict experienced by users. Responsibilization points to how users take individual responsibility for managing such conflicting flows, and to how this practice is difficult and conflict-ridden. We conclude that while individual time management is often framed as the solution to temporal conflicts, such attempts at regulating smartphone use appear inadequate. Our conceptualization of temporal ambivalence offers a more nuanced understanding of why this is the case.
Irene Costera Meijer; Tim Groot Kormelink
Changing News Use. Unchanged news experiences? Book
1st, Routledge, London & New York, 2020, ISBN: 9781003041719, (Pre SFI).
BibTeX | Links:
@book{Meijer2020,
title = {Changing News Use. Unchanged news experiences?},
author = {Irene Costera Meijer and Tim Groot Kormelink },
url = {https://www.researchgate.net/publication/345018999_Changing_News_Use_Unchanged_News_Experiences},
doi = {10.4324/9781003041719},
isbn = {9781003041719},
year = {2020},
date = {2020-09-01},
publisher = {Routledge},
address = {London & New York},
edition = {1st},
note = {Pre SFI},
keywords = {},
pubstate = {published},
tppubtype = {book}
}
Brita Ytre-Arne; Ranjana Das
Audiences’ Communicative Agency in a Datafied Age: Interpretative, Relational and Increasingly Prospective. Journal Article
In: Communication Theory, vol. 0, no. C, pp. 1-19, 2020, ISSN: 1050–3293, (Pre SFI).
@article{Arne2020c,
title = {Audiences’ Communicative Agency in a Datafied Age: Interpretative, Relational and Increasingly Prospective.},
author = {Brita Ytre-Arne and Ranjana Das},
url = {https://watermark.silverchair.com/qtaa018.pdf?token=AQECAHi208BE49Ooan9kkhW_Ercy7Dm3ZL_9Cf3qfKAc485ysgAAAtAwggLMBgkqhkiG9w0BBwagggK9MIICuQIBADCCArIGCSqGSIb3DQEHATAeBglghkgBZQMEAS4wEQQM-cFhCl8ql-5yUsJoAgEQgIICg0JoOiOVaCIdOstGjiiVpYuTKzqRfP7Hb1L0JBUB7TMpOQ5ya4v7afXtJvvTasi126A8qHSxK2rDZfeWFajUD34XXIbXVfimKI-a7-dZNYNjF6xn9p5OzsBABo10PuVtS5bHE4B3RSURKpRgGKXIyem7o-HzoTgKWKxJjRuOEVJNX4XjUC9-D9C7f8n3BItvYkMJqiX8NRSmuM3MI1MmhCjjtrUEOaURe-mCNKonobiYtkdoywElD9W7SG0ZQg9nigzcJmEH36Rbf3jzaGMMQhOsTv0NCAFwm52wxxeqt1jlSX6GmjMPmwTTmhZNGPE-sD0j3VZEfzqZNb5RqOBv2tih20z3kz2mBAU_25OlUThhSQ_G9-dNMVfVsuKMbo6TnS8EUotKdlWDmmenYUmQdHFx6KHA2mFn3j0AYyqz9Kolc2HeSSu4JJhZMZGHIUkNVC5c-GPPCzpO4D5pCT3pNw2d4mK037ziOWPOKcr29Ak2sJlQ05rcn4NovqBWrNIeRQRuhlVyNI7nr7bIXXFMwogvhFttUt3IxH68cYD6nNQ1Gbtl2BwygLseOwrwZA_6irZcqueVtAwqzMchowERFcKR8gMPKyDiDGPfbbKMNvqeHeIvemaQSLnfLM7Lfj9-OYtxOgF6CxZTrbOcCTUVpnvwTIZO1Oq2amSRBEb2lCORDiegqrxTitstUZ141VXW5bId94vwLuZqPoYA58nNcF0_2WzAOB69owF5B7D_ofDUTA2tBAhuvcvwKpAB5t_bhLDscuVfVGcYi5azd06oQ9PJWPV5EigzleuyHR57_cVyPpYkXe-PimZJ6iZzEhnmYZE0v9A_9F2LYm9dwfEryznsCog},
doi = {10.1093/ct/qtaa018},
issn = {1050–3293},
year = {2020},
date = {2020-08-24},
journal = {Communication Theory},
volume = {0},
number = {C},
pages = {1-19},
abstract = {This article develops a conceptualization of audience agency in the face of datafication. We consider how people, as audiences and users of media and technologies, face transforming communicative conditions, and how these conditions challenge the power potentials of audiences in processes of communication—that is, their communicative agency. To develop our conceptualization, we unpack the concept of audiences’ communicative agency by examining its foundations in communication scholarship, in reception theory and sociology, arguing that agency is understood as interpretative and relational, and applied to make important normative assessments. We further draw on emerging scholarship on encounters with data in the everyday to discuss how audience agency is now challenged by datafication, arguing that communicative agency is increasingly prospective in a datafied age. Thereby, we provide a theoretical conceptualization for further analysis of audiences in transforming communicative conditions.},
note = {Pre SFI},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
This article develops a conceptualization of audience agency in the face of datafication. We consider how people, as audiences and users of media and technologies, face transforming communicative conditions, and how these conditions challenge the power potentials of audiences in processes of communication—that is, their communicative agency. To develop our conceptualization, we unpack the concept of audiences’ communicative agency by examining its foundations in communication scholarship, in reception theory and sociology, arguing that agency is understood as interpretative and relational, and applied to make important normative assessments. We further draw on emerging scholarship on encounters with data in the everyday to discuss how audience agency is now challenged by datafication, arguing that communicative agency is increasingly prospective in a datafied age. Thereby, we provide a theoretical conceptualization for further analysis of audiences in transforming communicative conditions.
Krisztian Balog; Filip Radlinski
Measuring Recommendation Explanation Quality: The Conflicting Goals of Explanations Conference
Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '20), New York, 2020, (Pre SFI).
@conference{Balog2020,
title = {Measuring Recommendation Explanation Quality: The Conflicting Goals of Explanations},
author = {Krisztian Balog and Filip Radlinski},
url = {https://dl.acm.org/doi/pdf/10.1145/3397271.3401032},
doi = {https://doi.org/10.1145/3397271.3401032},
year = {2020},
date = {2020-07-01},
booktitle = {Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '20)},
pages = {329–338},
address = {New York},
abstract = {Explanations have a large effect on how people respond to recommendations. However, there are many possible intentions a system may have in generating explanations for a given recommendation -from increasing transparency, to enabling a faster decision, to persuading the recipient. As a good explanation for one goal may not be good for others, we address the questions of (1) how to robustly measure if an explanation meets a given goal and (2) how the different goals interact with each other. Specifically, this paper presents a first proposal of how to measure the quality of explanations along seven common goal dimensions catalogued in the literature. We find that the seven goals are not independent, but rather exhibit strong structure. Proposing two novel explanation evaluation designs, we identify challenges in evaluation, and provide more efficient measurement approaches of explanation quality.},
note = {Pre SFI},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
Explanations have a large effect on how people respond to recommendations. However, there are many possible intentions a system may have in generating explanations for a given recommendation -from increasing transparency, to enabling a faster decision, to persuading the recipient. As a good explanation for one goal may not be good for others, we address the questions of (1) how to robustly measure if an explanation meets a given goal and (2) how the different goals interact with each other. Specifically, this paper presents a first proposal of how to measure the quality of explanations along seven common goal dimensions catalogued in the literature. We find that the seven goals are not independent, but rather exhibit strong structure. Proposing two novel explanation evaluation designs, we identify challenges in evaluation, and provide more efficient measurement approaches of explanation quality.
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