GIS-based analysis of anti-Asian hate speech and its socioeconomic and ideological drivers in the United States during the early COVID-19 pandemic

Abstract Anti-Asian racism and xenophobia surged on social media during the COVID-19 pandemic, fueling discrimination and violence against Asians and Asian Americans in the United States. This study aims to understand the spatial patterns of Anti-Asian hate speech (AAHS) and identify the socioeconomic and ideological factors that may influence its emergence. Using Geographic Information System (GIS) tools, including hotspot analysis, Ordinary Least Squares (OLS), and Geographically Weighted Regression (GWR), we analyzed several socioeconomic and ideological factors to understand their impact on the spatial variability of AAHS risk levels. Our models show AAHS hotspots tend to cluster in suburban areas near the metropolitan and micropolitan regions. Our OLS model identifies three significant socioeconomic factors: the poverty level is negatively correlated with the AAHS risk level, but both high education level and diversity index demonstrate positive correlations with the AAHS risk level. The GWR model further explored local spatial patterns of how these factors influence AAHS levels. We suggest that anti-Asian sentiments may be driven by dominant groups fearing the loss of their identity and socioeconomic security. Additionally, in diverse areas, pandemic-related crises can exacerbate existing intergroup tensions, with structural inequalities further deepening divisions. Policymakers can leverage these findings to develop strategies aimed at mitigating the factors contributing to hate speech and effectively combating it. Future studies can expand on our findings to develop more robust theoretical frameworks for understanding these patterns.