Hate Speech Detection on Online News Platforms: A Deep-Learning Approach Based on Agenda-Setting Theory
将议程设置理论与设计科学结合，利用BERT深度学习模型分析新闻标题、正文和用户评论的互动，提升在线新闻平台仇恨言论检测的准确性和可解释性。
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.