Effective response to pandemics requires coordinated adoption of mitigation measures, like masking and quarantines, to curb a virus's spread. However, as the COVID-19 pandemic demonstrated, political divisions can hinder consensus on the appropriate response. To better understand these divisions, our study examines a vast collection of COVID-19-related tweets. We focus on five contentious issues: coronavirus origins, lockdowns, masking, education, and vaccines. We describe a weakly supervised method to identify issue-relevant tweets and employ state-of-the-art computational methods to analyze moral language and infer political ideology. We explore how partisanship and moral language shape conversations about these issues. Our findings reveal ideological differences in issue salience and moral language used by different groups. We find that conservatives use more negatively-valenced moral language than liberals and that political elites use moral rhetoric to a greater extent than non-elites across most issues. Examining the evolution and moralization on divisive issues can provide valuable insights into the dynamics of COVID-19 discussions and assist policymakers in better understanding the emergence of ideological divisions.

翻译：应对大流行病需要协调采取缓解措施（如戴口罩和隔离）以遏制病毒传播。然而，正如COVID-19大流行所示，政治分歧可能阻碍对适当应对措施的共识。为更好地理解这些分歧，本研究分析了大量与COVID-19相关的推文。我们聚焦于五个争议性议题：冠状病毒起源、封锁措施、戴口罩、教育和疫苗。我们描述了一种弱监督方法来识别与议题相关的推文，并采用最先进的计算方法分析道德语言及推断政治意识形态。我们探讨了党派立场与道德语言如何塑造围绕这些议题的讨论。研究结果揭示了不同群体在议题凸显性和道德语言使用上的意识形态差异。我们发现，保守派比自由派使用更多负向情感色彩的道德语言，而在大多数议题上，政治精英比非精英更广泛地运用道德修辞。审视争议性议题的演变与道德化过程，可为理解COVID-19讨论的动态提供重要洞见，并帮助政策制定者更深入地认识意识形态分歧的成因。