Gloria Anne Babile Kasangu
Research Assistant
University of Bergen
Gloria Anne Babile Kasangu is a Research Assistant at MediaFutures. She’s has a Bachelor’s degree in Information Science at the University of Bergen, and a Bachelor’s degree in General Psychology. She is currently taking her Master in Information Science at the UiB. When she’s not studying or working, she enjoys cooking, writing, and working out.
2025
Kasangu, Gloria Anne Babile; Starke, Alain D.; Trattner, Christoph
More of the Same? A Longitudinal Evaluation of Two Similarity-based Approaches in a News Recommender System Proceedings Article
In: Proceedings of the 13th International Workshop on News Recommendation and Analytics (INRA 2025), 2025.
@inproceedings{morefothesame,
title = {More of the Same? A Longitudinal Evaluation of Two Similarity-based Approaches in a News Recommender System},
author = {Gloria Anne Babile Kasangu and Alain D. Starke and Christoph Trattner},
url = {https://mediafutures.no/recsys2025_inra_longitudinal_news___ceur/},
year = {2025},
date = {2025-10-01},
booktitle = {Proceedings of the 13th International Workshop on News Recommendation and Analytics (INRA 2025)},
abstract = {Similarity-based personalization is generally assumed to boost engagement in recommender systems. However, is
this also true beyond a single session in a news recommender? Amid concerns about filter bubbles and preference volatility, we propose an empirical evaluation of both short-term and longer-term effects of a news recommender system with two phases of data collection: Initial preference elicitation and evaluation (Phase 1), a 48-hour interval, and a personalized follow-up (Phase 2). We compared two recommendation strategies in a preliminary longitudinal experiment (? = 166): An ‘Aligned’ feed that included articles that met a ≥ 70% cosine‐similarity threshold, and a ‘Disaligned’ feed with only a 30% similarity threshold. We collected behavioral metrics (article clicks, time on feed) and evaluative metrics (self-reported familiarity, perceived recommendation quality, choice satisfaction, topic preferences) in both phases. The Aligned feed was perceived to have more familiar content, while perceived diversity did not differ between recommendation strategies. Users clicked on significantly fewer articles in Phase 2, particularly in the Disaligned condition. We also explored the volatility of topic preferences, but did not observe significant differences across phases. These findings suggest that short-term increases in feed–profile similarity can enhance familiarity and maintain behavioral engagement (i.e., clicks). In contrast, they do not lead to higher levels of perceived quality and choice satisfaction, which raises doubts about the relationship between the similarity of preference-based articles and user satisfaction.},
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pubstate = {published},
tppubtype = {inproceedings}
}
Similarity-based personalization is generally assumed to boost engagement in recommender systems. However, is
this also true beyond a single session in a news recommender? Amid concerns about filter bubbles and preference volatility, we propose an empirical evaluation of both short-term and longer-term effects of a news recommender system with two phases of data collection: Initial preference elicitation and evaluation (Phase 1), a 48-hour interval, and a personalized follow-up (Phase 2). We compared two recommendation strategies in a preliminary longitudinal experiment (? = 166): An ‘Aligned’ feed that included articles that met a ≥ 70% cosine‐similarity threshold, and a ‘Disaligned’ feed with only a 30% similarity threshold. We collected behavioral metrics (article clicks, time on feed) and evaluative metrics (self-reported familiarity, perceived recommendation quality, choice satisfaction, topic preferences) in both phases. The Aligned feed was perceived to have more familiar content, while perceived diversity did not differ between recommendation strategies. Users clicked on significantly fewer articles in Phase 2, particularly in the Disaligned condition. We also explored the volatility of topic preferences, but did not observe significant differences across phases. These findings suggest that short-term increases in feed–profile similarity can enhance familiarity and maintain behavioral engagement (i.e., clicks). In contrast, they do not lead to higher levels of perceived quality and choice satisfaction, which raises doubts about the relationship between the similarity of preference-based articles and user satisfaction.
this also true beyond a single session in a news recommender? Amid concerns about filter bubbles and preference volatility, we propose an empirical evaluation of both short-term and longer-term effects of a news recommender system with two phases of data collection: Initial preference elicitation and evaluation (Phase 1), a 48-hour interval, and a personalized follow-up (Phase 2). We compared two recommendation strategies in a preliminary longitudinal experiment (? = 166): An ‘Aligned’ feed that included articles that met a ≥ 70% cosine‐similarity threshold, and a ‘Disaligned’ feed with only a 30% similarity threshold. We collected behavioral metrics (article clicks, time on feed) and evaluative metrics (self-reported familiarity, perceived recommendation quality, choice satisfaction, topic preferences) in both phases. The Aligned feed was perceived to have more familiar content, while perceived diversity did not differ between recommendation strategies. Users clicked on significantly fewer articles in Phase 2, particularly in the Disaligned condition. We also explored the volatility of topic preferences, but did not observe significant differences across phases. These findings suggest that short-term increases in feed–profile similarity can enhance familiarity and maintain behavioral engagement (i.e., clicks). In contrast, they do not lead to higher levels of perceived quality and choice satisfaction, which raises doubts about the relationship between the similarity of preference-based articles and user satisfaction.
Starke, Alain D.; Dierkes, Jutta; Lied, Gülen Arslan; Kasangu, Gloria Anne Babile; Trattner, Christoph
Supporting healthier food choices through AI-tailored advice: A research agenda Journal Article
In: PEC Innovation, 2025.
@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},
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pubstate = {published},
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Jeng, Jia Hua; Kasangu, Gloria Anne Babile; Starke, Alain D.; Seddik, Khadiga; Trattner, Christoph
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.},
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pubstate = {published},
tppubtype = {conference}
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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.
2024
Kasangu, Gloria Anne Babile; Starke, Alain D.; Nilsen, Anna; Trattner, Christoph
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},
tppubtype = {conference}
}
Users of social media platforms face concerns about the accuracy and reliability of information shared on it. This includes images being shared online, which are often linked to news events. This study investigates what effects Instagram filters
have on users’ perceived trust of online news posts that include images. Trust ratings of four different articles across four image filter conditions were obtained in an online user study (N=204). We also inquired on a user's general trust and familiarity with the news topic Also, the role of general trust and familiarity with the topic. Our analysis revealed that while Instagram filters overall may not affect perceived trust, specific visual characteristics of the filters such as brightness and
contrast affected trust levels. Additionally, individual differences in general trust and attitude towards a specific topic may influence the users’ perception of trust.
have on users’ perceived trust of online news posts that include images. Trust ratings of four different articles across four image filter conditions were obtained in an online user study (N=204). We also inquired on a user's general trust and familiarity with the news topic Also, the role of general trust and familiarity with the topic. Our analysis revealed that while Instagram filters overall may not affect perceived trust, specific visual characteristics of the filters such as brightness and
contrast affected trust levels. Additionally, individual differences in general trust and attitude towards a specific topic may influence the users’ perception of trust.
Jeng, Jia Hua; Kasangu, Gloria Anne Babile; Starke, Alain D.; Knudsen, Erik; Trattner, Christoph
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.
Jeng, Jia Hua; Kasangu, Gloria Anne Babile; Starke, Alain D.; Trattner, Christoph
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