Schadenfreude in the Digital Public Sphere: A cross-national and decade-long analysis of Facebook news engagement

TL;DR

The paper investigates schadenfreude in the digital public sphere by analyzing a decade of Facebook news engagement across nine outlets in the US, UK, and India. It combines human annotations with LLM-based classification to identify misfortune posts and to extract schadenfreude signals from nearly a million comments, across three ideological lanes. The results show that schadenfreude is most prevalent in moral and political contexts, higher among right-leaning audiences and more pronounced in India, and that its expression evolves with political power and time, with distinct cross-national patterns. The work highlights the role of affective emotions in online political discourse and raises implications for polarization and platform design.

Abstract

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.

Figures (11)

• Figure 1: (A) The distribution of Love, Care, HaHa, Wow, Sad, and Angry reactions on Facebook posts describing others’ misfortunes. Panels B–D show variation across left-, center-, and right-leaning outlets, while Panels E–G compare reactions across India, the United States, and the United Kingdom.
• Figure 2: The proportion of comments exhibiting schadenfreude across outlets of different countries (Left panel) and ideological leanings (Right panel).
• Figure 3: The average schadenfreude rate per post for each post topic, disaggregated by country-leaning pair.
• Figure 4: The average schadenfreude rate over time disaggregated by country and source political leaning.
• Figure 5: OLS regression estimating the schadenfreude rate as a function of the political leaning of the source of the post, the party in power when the post was made, and the topic of the post in question.