Jia-Hua Jeng
PhD Candidate
University of Bergen
2025
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.},
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.
2024
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
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.
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
2023
Jeng, Jia Hua; Starke, Alain D.; Trattner, Christoph
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.