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Abstract The purpose of the study was to explore differences in Google search autocompletes between English and Spanish‐speaking users during the first wave of the coronavirus disease 2019 (COVID‐19) pandemic. Twenty‐nine individuals who were in areas with shelter‐in‐place state orders participated in a virtual focus group meeting to understand the algorithm bias of COVID‐19 Google autocompletes. The three focus group meetings lasted for 90–120 minutes. A codebook was created and transcripts were coded using NVivo qualitative software with a 95% intercoder reliability between two coders. Thematic analysis was used to analyze the data. Among the 29 participants, six self‐identified as White, seven as Black/African American, five as American Indian or Alaska Native, four as Asian Indian, and three as Native Hawaiian or Pacific Islander. In terms of ethnicity, 21 participants identified as Hispanic/Latino. The themes that emerged from the study were: (1) autocompletes evoked fear and stress; (2) skepticism and hesitation towards autocomplete search; (3) familiarity with COVID‐19 information impacts outlook on autocomplete search; (4) autocompletes can promote preselection of searches; and (5) lesser choice of autocomplete results for Spanish‐speaking searchers. Spanish speakers expressed concerns and hesitation due to social factors and lack of information about COVID‐19. 

Abstract As the impact of the COVID‐19 pandemic grew in 2020, uncertainty surrounding its origins and nature led to widespread conspiracy‐related theories (CRT). Use of technological platforms enabled the rapid and exponential dissemination of COVID‐19 CRT. This study applies social contagion theory to examine how Google Autocomplete (GA) propagates and perpetuates these CRT. An in‐house software program, Autocomplete Search Logging Tool (ASLT) captured a snapshot of GA COVID‐19 related searches early in the pandemic (from March to May 2020) across 76 randomly‐selected countries to gain insight into search behaviors around the world. Analysis identified 15 keywords relating to COVID‐19 CRT predictions and demonstrate how searches across different countries received varying degrees of GA predictions. When grouped with similar keywords, two major categories were identified “Man‐Made Biological Weapon” (42%, n = 2,111), and “Questioning Reality/Severity of COVID‐19” (44%, n = 2,224). This investigation is also among the first to apply social contagion theory to autocomplete applications and can be used in future research to explain and perhaps mitigate the spread of CRT.