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Emotional Reframing of Economic News using a Large Language Model

Authors:

Gloria Kasangu,

Christoph TrattnerAuthors Info & Claims

UMAP Adjunct '24: Adjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization

Pages 231 - 235

https://doi.org/10.1145/3631700.3665191

Published: 28 June 2024 Publication History

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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.

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Cited By

Yang GXie QChen L(2024)A Scientometrics Analysis and Visualization of Large Language Model in China's LibraryProceeding of the 2024 5th International Conference on Computer Science and Management Technology10.1145/3708036.3708272(1403-1407)Online publication date: 18-Oct-2024
https://dl.acm.org/doi/10.1145/3708036.3708272
Jeng J(2024)Bridging Viewpoints in News with Recommender SystemsProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688008(1283-1289)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3688008

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Published In

cover image ACM Conferences

UMAP Adjunct '24: Adjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization

June 2024

662 pages

ISBN:9798400704666

DOI:10.1145/3631700

General Chairs:
Ludovico Boratto
University of Cagliari, Italy
,
Cristina Gena
University of Turin, Italy
,
Mirko Marras
University of Cagliari, Italy
,
Program Chairs:
Panagiotis Germanakos
SAP SE, Germany
,
Elvira Popescus
University of Craiova, Romania

Copyright © 2024 Owner/Author.

This work is licensed under a Creative Commons Attribution International 4.0 License.

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New York, NY, United States

Publication History

Published: 28 June 2024

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UMAP '24

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UMAP '24: 32nd ACM Conference on User Modeling, Adaptation and Personalization

July 1 - 4, 2024

Cagliari, Italy

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Overall Acceptance Rate 162 of 633 submissions, 26%

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Cited By

Yang GXie QChen L(2024)A Scientometrics Analysis and Visualization of Large Language Model in China's LibraryProceeding of the 2024 5th International Conference on Computer Science and Management Technology10.1145/3708036.3708272(1403-1407)Online publication date: 18-Oct-2024
https://dl.acm.org/doi/10.1145/3708036.3708272
Jeng J(2024)Bridging Viewpoints in News with Recommender SystemsProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688008(1283-1289)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3688008

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