分析视频评论中的仇恨言论与情绪，发现公开源更易出现仇恨言论。
Hate Speech and Sentiment of YouTube Video Comments From Public and Private Sources Covering the Israel-Palestine Conflict
- 结合人工标注与机器学习模型分析评论内容
- 公开来源仇恨言论占比40.4%，高于私有来源的31.6%
- 适用于研究网络政治争议中内容安全与舆论倾向
本研究通过分析来自公开与私有新闻来源的YouTube视频评论，探讨以色列-巴勒斯坦冲突相关评论中仇恨言论（HS）与情感倾向的分布。研究对4983条评论进行了人工标注，涵盖仇恨言论及中立、亲以、亲巴三种情感类别。随后构建机器学习（ML）模型，其受试者工作特征曲线下面积（AUROC）在0.83至0.90之间，表现稳健。模型应用于两类来源的评论数据，发现公开来源的仇恨言论比例达40.4%，显著高于私有来源的31.6%。情感分析显示，两类来源均以中立立场为主，但公开来源对以色列和巴勒斯坦的情绪倾向更明显。研究揭示了该议题在线讨论的动态特征，并凸显在政治敏感环境中内容治理的重要性。
原文摘要 · Abstract (English)
This study explores the prevalence of hate speech (HS) and sentiment in YouTube video comments concerning the Israel-Palestine conflict by analyzing content from both public and private news sources. The research involved annotating 4983 comments for HS and sentiments (neutral, pro-Israel, and pro-Palestine). Subsequently, machine learning (ML) models were developed, demonstrating robust predictive capabilities with area under the receiver operating characteristic (AUROC) scores ranging from 0.83 to 0.90. These models were applied to the extracted comment sections of YouTube videos from public and private sources, uncovering a higher incidence of HS in public sources (40.4%) compared to private sources (31.6%). Sentiment analysis revealed a predominantly neutral stance in both source types, with more pronounced sentiments towards Israel and Palestine observed in public sources. This investigation highlights the dynamic nature of online discourse surrounding the Israel-Palestine conflict and underscores the potential of moderating content in a politically charged environment.
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