In EDS ansehen

Hate Speech Detection on Online News Platforms: A Deep-Learning Approach Based on Agenda-Setting Theory.

Gespeichert in:
Bibliographische Detailangaben
Titel: Hate Speech Detection on Online News Platforms: A Deep-Learning Approach Based on Agenda-Setting Theory.
Autoren: Kim, Seong-Su1 (AUTHOR) fenzhantw@gmail.com, Kim, Seongbeom2 (AUTHOR) sb_kim@yonsei.ac.kr, Kim, Hee-Woong2 (AUTHOR) kimhw@yonsei.ac.kr
Quelle: Journal of Management Information Systems. 2025, Vol. 42 Issue 3, p673-705. 33p.
Schlagwörter: *INFORMATION technology, HATE speech, AGENDA setting theory (Communication), DEEP learning, LANGUAGE models, NEWS websites, CONTEXTUAL analysis
Abstract: Hate speech on online news platforms has emerged as a critical societal challenge, influencing public discourse and impacting industries. However, existing detection methods often fail to capture its contextual nature and are grounded in weak theoretical foundations. To address this gap, we integrate agenda-setting theory with design science paradigms to develop a deep learning model for detecting hate speech on online news platforms. Leveraging bidirectional encoder representations from transformers (BERT), our model analyzes the interplay between news headlines, texts, and user comments, capturing both media issue salience and emotional agenda-setting effects. Empirical validation demonstrates that our model outperforms traditional baselines in hate speech detection and topic classification tasks. This contextualized approach enhances prediction accuracy, explainability, and domain adaptability, contributing to improved performance and broader applicability. Our study makes theoretical and methodological contributions to Information Systems research and offers practical insights for implementing ethical, real-time hate speech detection strategies. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Management Information Systems is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Datenbank: Business Source Index
Beschreibung
Abstract:Hate speech on online news platforms has emerged as a critical societal challenge, influencing public discourse and impacting industries. However, existing detection methods often fail to capture its contextual nature and are grounded in weak theoretical foundations. To address this gap, we integrate agenda-setting theory with design science paradigms to develop a deep learning model for detecting hate speech on online news platforms. Leveraging bidirectional encoder representations from transformers (BERT), our model analyzes the interplay between news headlines, texts, and user comments, capturing both media issue salience and emotional agenda-setting effects. Empirical validation demonstrates that our model outperforms traditional baselines in hate speech detection and topic classification tasks. This contextualized approach enhances prediction accuracy, explainability, and domain adaptability, contributing to improved performance and broader applicability. Our study makes theoretical and methodological contributions to Information Systems research and offers practical insights for implementing ethical, real-time hate speech detection strategies. [ABSTRACT FROM AUTHOR]
ISSN:07421222
DOI:10.1080/07421222.2025.2520173