Schadenfreude, or the pleasure derived from others' misfortunes, has become a visible and performative feature of online news engagement, yet little is known about its prevalence, dynamics, or social patterning. We examine schadenfreude on Facebook over a ten-year period across nine major news publishers in the United States, the United Kingdom, and India (one left-leaning, one right-leaning, and one centrist per country). Using a combination of human annotation and machine-learning classification, we identify posts describing misfortune and detect schadenfreude in nearly one million associated comments. We find that while sadness and anger dominate reactions to misfortune posts, laughter and amusement form a substantial and patterned minority. Schadenfreude is most frequent in moralized and political contexts, higher among right-leaning audiences, and more pronounced in India than in the United States or United Kingdom. Temporal and regression analyses further reveal that schadenfreude generally increases when groups are politically out of power, but these patterns differ across party lines. Together, our findings move beyond anecdotal accounts to map schadenfreude as a dynamic, context-dependent feature of digital discourse, revealing how it evolves over time and across ideological and cultural divides.

翻译：幸灾乐祸，即从他人的不幸中获得愉悦，已成为在线新闻互动中一个显著且具有表演性的特征，但其普遍性、动态变化或社会模式却鲜为人知。我们研究了Facebook上十年间美国、英国和印度九家主要新闻出版商（每个国家各一家左倾、一家右倾和一家中立媒体）内容中的幸灾乐祸现象。通过结合人工标注和机器学习分类，我们识别了描述不幸事件的帖子，并在近百万条相关评论中检测了幸灾乐祸情绪。我们发现，尽管悲伤和愤怒是对不幸事件帖子的主要反应，但大笑和娱乐构成了一个显著且具有特定模式的少数反应。幸灾乐祸最常出现在道德化和政治化的语境中，在右倾受众中更为普遍，并且在印度比在美国或英国更为突出。时间序列分析和回归分析进一步揭示，当群体在政治上失势时，幸灾乐祸情绪通常会上升，但这些模式在不同党派阵营之间存在差异。总之，我们的研究超越了轶事描述，将幸灾乐祸描绘为数字话语中一种动态的、依赖于语境的特征，揭示了它如何随着时间推移以及跨越意识形态和文化鸿沟而演变。