The language of protest: A scientific mapping of cross-national protest themes, narratives, and success patterns

Protests channel political claims by mobilizing publics against perceived injustice. Yet large-N, cross-national analyses remain scarce. We analyse 602 protest events (2018-2023) from the Carnegie dataset using scientific mapping and computational text analysis (VOSviewer). Five thematic clusters emerge: economic grievances; governance failures; racial/social justice; authoritarian repression; and labour rights. We define success as a policy change aligned with protesters’ demands and examine how themes relate to reported outcomes. Economic and labour protests exhibit comparatively higher short-term policy gains, whereas anti-authoritarian and racial justice mobilizations more often face repression. Building on these findings, the study proposes the Converging Grievance Model as a heuristic framework that highlights the interconnections between grievance convergence, coalitional capacity and political receptivity.