Schadenfreude in the Digital Public Sphere: A Cross-National, Decade-Long Analysis of Facebook News Engagement
Abstract
Pleasure at the misfortune of others, or schadenfreude, is widely believed to have become a routine feature of online news audiences, yet its prevalence and social patterning have rarely been measured at scale. Here, we examine ten years (2015-2024) of Facebook engagement with misfortune news across nine major outlets spanning the political spectrum in the United States, the United Kingdom, and India. We separate schadenfreude, in which a commenter expresses pleasure at the misfortune, from hostility without pleasure, using an ensemble of three open-weight language models validated against human annotation, and classify 1.06 million post-comment pairs. We find that schadenfreude constitutes 26% of comments on news covering misfortune, a third more than the rate implied by "Haha" reactions on the same posts, and that the two measures agree at the post level (Spearman $ρ= 0.43$) while hostility does not ($ρ= 0.07$). Schadenfreude varies by less than one percentage point across the three countries and concentrates where theories of deserved suffering predict: on misfortunes framed as self-inflicted, on scandals and defeats rather than deaths, and on public figures rather than ordinary people. Hostility, in contrast, varies widely across outlets and responds to politics. Audiences of a left-leaning US outlet became 5.5 percentage points less hostile while their party held the presidency, a pattern robust across specifications, whereas no comparable power effect on schadenfreude survives robustness checks. Cross-national differences that a toxicity-based measure attributes to schadenfreude turn out, under the pleasure-explicit measure, to be differences in hostility. These results show that schadenfreude is a measurable mode of news engagement, distinct from toxicity and patterned by the moral structure of misfortune rather than by country or political power.
Fields of Study & Subjects
Misinformation and Its Impacts
Humor Studies and Applications
Sentiment Analysis and Opinion Mining
Publication details
- Journal / source
- arXiv (Cornell University)
- Publication date
- 28 January 2026
- Document type
- Preprint