This article maps political rhetoric by national leaders during the COVID‐19 pandemic. We identify and characterize global variations in major rhetorical storylines invoked in publicly available speeches (N = 1201) across a sample of 26 countries. Employing a text analytics or corpus linguistics approach, we show that state heads rhetorically lead their nations by: enforcing systemic interventions, upholding global unity, encouraging communal cooperation, stoking national fervor, and assuring responsive governance. Principal component analysis further shows that country‐level rhetoric is organized along emergent dimensions of cultural cognition: an agency‐structure axis to define the loci of pandemic interventions and a hierarchy‐egalitarianism axis which distinguishes top‐down enforcement from bottom‐up calls for cooperation. Furthermore, we detect a striking contrast between countries featuring populist versus cosmopolitan rhetoric, which diverged in terms of their collective meaning making around leading over versus leading with, as well as their experienced pandemic severity. We conclude with implications for understanding global pandemic leadership in an unequal world and the contributions of mixed‐methods approaches to a generative political psychology in times of crisis. During a global pandemic, political leaders must adapt their rhetoric to local societal conditions. Our work affirms the importance of political governance that elevates strong institutions while empowering public cooperation, but cautions that these may be most readily enacted in more democratic and economically developed contexts. In poorer, less democratic settings, rhetoric emphasizing accountable governance should be responsive to bottom‐up grassroots efforts in local communities, in contrast to the tightening grip of top‐down militarized policing in states witnessing the opportunistic creep of authoritarianism during a period of societal disorder. Finally, rhetoric where leaders uphold global ideals underscore wider identities of international collaborations in a global crisis, in contrast to more insularizing nationalistic rhetoric.

Online hate speech represents a serious problem exacerbated by the ongoing COVID-19 pandemic. Although often anchored in real-world social divisions, hate speech in cyberspace may also be fueled inorganically by inauthentic actors like social bots. This work presents and employs a methodological pipeline for assessing the links between hate speech and bot-driven activity through the lens of social cybersecurity. Using a combination of machine learning and network science tools, we empirically characterize Twitter conversations about the pandemic in the United States and the Philippines. Our integrated analysis reveals idiosyncratic relationships between bots and hate speech across datasets, highlighting different network dynamics of racially charged toxicity in the US and political conflicts in the Philippines. Most crucially, we discover that bot activity is linked to higher hate in both countries, especially in communities which are denser and more isolated from others. We discuss several insights for probing issues of online hate speech and coordinated disinformation, especially through a global approach to computational social science.

Hate speech has long posed a serious problem for the integrity of digital platforms. Although significant progress has been made in identifying hate speech in its various forms, prevailing computational approaches have tended to consider it in isolation from the community-based contexts in which it spreads. In this paper, we propose a dynamic network framework to characterize hate communities, focusing on Twitter conversations related to COVID-19 in the United States and the Philippines. While average hate scores remain fairly consistent over time, hate communities grow increasingly organized in March, then slowly disperse in the succeeding months. This pattern is robust to fluctuations in the number of network clusters and average cluster size. Infodemiological analysis demonstrates that in both countries, the spread of hate speech around COVID-19 features similar reproduction rates as other COVID-19 information on Twitter, with spikes in hate speech generation at time points with highest community-level organization of hate speech. Identity analysis further reveals that hate in the US initially targets political figures, then grows predominantly racially charged; in the Philippines, targets of hate consistently remain political over time. Finally, we demonstrate that higher levels of community hate are consistently associated with smaller, more isolated, and highly hierarchical network clusters across both contexts. This suggests potentially shared structural conditions for the effective spread of hate speech in online communities even when functionally targeting distinct identity groups. Our findings bear theoretical and methodological implications for the scientific study of hate speech and understanding the pandemic’s broader societal impacts both online and offline.

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