Language disparities in pandemic information: Autocomplete analysis of COVID-19 searches in New York

Abstract

Objective: To audit and compare search autocomplete results in Spanish and English during the early COVID-19 pandemic in the New York metropolitan area. The pandemic led to significant online search activity about the disease, its spread, and remedies. As gatekeepers, search engines like Google can influence public opinion. Autocomplete predictions help users complete searches faster but may also shape their views. Understanding these differences is crucial to identify biases and ensure equitable information dissemination. Methods: The study tracked autocomplete results daily for five COVID-19 related search terms in English and Spanish over 100+ days in 2020, yielding a total of 9164 autocomplete predictions. Results: Queries in Spanish yielded fewer autocomplete options and often included more negative content than English autocompletes. The topical coverage differed, with Spanish autocompletes including themes related to religion and spirituality that were absent in the English search autocompletes. Conclusion: The contrast in search autocomplete results could lead to divergent impressions about the pandemic and remedial actions among different sections of society. Continuous auditing of autocompletes by public health stakeholders and search engine organizations is recommended to reduce potential bias and misinformation.