Economic bifurcations in pandemic leadership: Power in abundance or agency amid scarcity?

Social psychological scholarship has emphasized the importance of effective leadership during the COVID-19 pandemic. However, the wider material contexts of these dynamics have often remained understudied. Through a critical discursive lens, this paper investigates differences in the social constructions used by leaders of richer and poorer nations during the COVID-19 pandemic. We identify a sharp economic bifurcation in global discourses of pandemic leadership. Pandemic leadership in wealthier nations exercises power in abundance by mobilizing institutions and inspiring communities through discursive frames of coordination and collaboration. Conversely, pandemic leadership in poorer settings negotiates agency amid scarcity by tactically balancing resources, freedoms and dignity within discursive frames of restriction and recuperation. Implications of these findings are unpacked for understanding leadership especially during an international crisis, highlighting the need for critical sensitivities to wider societal structures for a genuinely global social psychology.

Policy-driven mathematical modeling for COVID-19 pandemic response in the Philippines.

Around the world, disease surveillance and mathematical modeling have been vital tools for government responses to the COVID-19 pandemic. In the face of a volatile crisis, modeling efforts have had to evolve over time in proposing policies for pandemic interventions. In this paper, we document how mathematical modeling contributed to guiding the trajectory of pandemic policies in the Philippines. We present the mathematical specifications of the FASSSTER COVID-19 compartmental model at the core of the FASSSTER platform, the scenario-based disease modeling and analytics toolkit used in the Philippines. We trace how evolving epidemiological analysis at the national, regional, and provincial levels guided government actions; and conversely, how emergent policy questions prompted subsequent model development and analysis. At various stages of the pandemic, simulated outputs of the FASSSTER model strongly correlated with empirically observed case trajectories (r=94%-99%, p<.001). Model simulations were subsequently utilized to predict the outcomes of proposed interventions, including the calibration of community quarantine levels alongside improvements to healthcare system capacity. This study shows how the FASSSTER model enabled the implementation of a phased approach toward gradually expanding economic activity while limiting the spread of COVID-19. This work points to the importance of locally contextualized, flexible, and responsive mathematical modeling, as applied to pandemic intelligence and for data-driven policy-making in general.

Mapping state-sponsored information operations with multi-view modularity clustering.

This paper presents a new computational framework for mapping state-sponsored information operations into distinct strategic units. Utilizing a novel method called multi-view modularity clustering (MVMC), we identify groups of accounts engaged in distinct narrative and network information maneuvers. We then present an analytical pipeline to holistically determine their coordinated and complementary roles within the broader digital campaign. Applying our proposed methodology to disclosed Chinese state-sponsored accounts on Twitter, we discover an overarching operation to protect and manage Chinese international reputation by attacking individual adversaries (Guo Wengui) and collective threats (Hong Kong protestors), while also projecting national strength during global crisis (the COVID-19 pandemic). Psycholinguistic tools quantify variation in narrative maneuvers employing hateful and negative language against critics in contrast to communitarian and positive language to bolster national solidarity. Network analytics further distinguish how groups of accounts used network maneuvers to act as balanced operators, organized masqueraders, and egalitarian echo-chambers. Collectively, this work breaks methodological ground on the interdisciplinary application of unsupervised and multi-view methods for characterizing not just digital campaigns in particular, but also coordinated activity more generally. Moreover, our findings contribute substantive empirical insights around how state-sponsored information operations combine narrative and network maneuvers to achieve interlocking strategic objectives. This bears both theoretical and policy implications for platform regulation and understanding the evolving geopolitical significance of cyberspace.

Impact of vaccine supplies and delays on optimal control of the COVID-19 pandemic: mapping interventions for the Philippines.

BACKGROUND: Around the world, controlling the COVID-19 pandemic requires national coordination of multiple intervention strategies. As vaccinations are globally introduced into the repertoire of available interventions, it is important to consider how changes in the local supply of vaccines, including delays in administration, may be addressed through existing policy levers. This study aims to identify the optimal level of interventions for COVID-19 from 2021 to 2022 in the Philippines, which as a developing country is particularly vulnerable to shifting assumptions around vaccine availability. Furthermore, we explore optimal strategies in scenarios featuring delays in vaccine administration, expansions of vaccine supply, and limited combinations of interventions. METHODS: Embedding our work within the local policy landscape, we apply optimal control theory to the compartmental model of COVID-19 used by the Philippine government's pandemic surveillance platform and introduce four controls: (a) precautionary measures like community quarantines, (b) detection of asymptomatic cases, (c) detection of symptomatic cases, and (d) vaccinations. The model is fitted to local data using an L-BFGS minimization procedure. Optimality conditions are identified using Pontryagin's minimum principle and numerically solved using the forward-backward sweep method. RESULTS: Simulation results indicate that early and effective implementation of both precautionary measures and symptomatic case detection is vital for averting the most infections at an efficient cost, resulting in [Formula: see text] reduction of infections compared to the no-control scenario. Expanding vaccine administration capacity to 440,000 full immunizations daily will reduce the overall cost of optimal strategy by [Formula: see text], while allowing for a faster relaxation of more resource-intensive interventions. Furthermore, delays in vaccine administration require compensatory increases in the remaining policy levers to maintain a minimal number of infections. For example, delaying the vaccines by 180 days (6 months) will result in an [Formula: see text] increase in the cost of the optimal strategy. CONCLUSION: We conclude with practical insights regarding policy priorities particularly attuned to the Philippine context, but also applicable more broadly in similar resource-constrained settings. We emphasize three key takeaways of (a) sustaining efficient case detection, isolation, and treatment strategies; (b) expanding not only vaccine supply but also the capacity to administer them, and; (c) timeliness and consistency in adopting policy measures.

Active, aggressive, but to little avail: characterizing bot activity during the 2020 Singaporean elections.

Digital disinformation presents a challenging problem for democracies worldwide, especially in times of crisis like the COVID-19 pandemic. In countries like Singapore, legislative efforts to quell fake news constitute relatively new and understudied contexts for understanding local information operations. This paper presents a social cybersecurity analysis of the 2020 Singaporean elections, which took place at the height of the pandemic and after the recent passage of an anti-fake news law. Harnessing a dataset of 240,000 tweets about the elections, we found that 26.99% of participating accounts were likely to be bots, responsible for a larger proportion of bot tweets than the election in 2015. Textual analysis further showed that the detected bots used simpler and more abusive second-person language, as well as hashtags related to COVID-19 and voter activity-pointing to aggressive tactics potentially fuelling online hostility and questioning the legitimacy of the polls. Finally, bots were associated with larger, less dense, and less echo chamber-like communities, suggesting efforts to participate in larger, mainstream conversations. However, despite their distinct narrative and network maneuvers, bots generally did not hold significant influence throughout the social network. Hence, although intersecting concerns of political conflict during a global pandemic may promptly raise the possibility of online interference, we quantify both the efforts and limits of bot-fueled disinformation in the 2020 Singaporean elections. We conclude with several implications for digital disinformation in times of crisis, in the Asia-Pacific and beyond.

The Language of Pandemic Leaderships: Mapping Political Rhetoric During the COVID-19 Outbreak.

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.

Characterizing network dynamics of online hate communities around the COVID-19 pandemic.

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.

Bots and online hate during the COVID-19 pandemic: case studies in the United States and the Philippines.

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.