麻豆传媒

News — Rutgers School of Public Health assistant professor, , and Rutgers School of Communication and Information assistant professor, , have received a grant to analyze the differences in COVID-19 related online searches for English and Spanish speaking users.

The project, “,” will run from May 1, 2020, through April 30, 2021, with Valera serving as the project’s co-primary investigator. Along with Singh, the project’s primary investigator, they will audit COVID-19 related online search results in both English and Spanish to identify any differences, and make recommendations for equitable health information dissemination.

The COVID-19 pandemic has resulted in a sharp increase in online search activity about the disease, its spread, and counteractive actions on search engines, like Google, significantly influencing public perception and any actions taken to prevent, treat, or mitigate the disease.

The researchers will examine “autocomplete” results, a search engine feature that allows users to complete searches faster by populating information in the search engine’s text box as they type. The feature, while convenient, can contribute to bias that if left unchecked, has the potential to lead to health disparities  experienced by marginalized and minority groups by providing different results for similar inquiries.

“By examining autocompletes in search engines, we can understand how bias and misinformation contribute to  health inequalities,” said Valera.

Previous pilot research conducted by Singh has shown that autocompletes for terms related to COVID-19 were different in English and Spanish.

“For instance, in the first three auto-complete results for "coronavirus is ...," the English version had one question and two neutral-sounding statements. For the Spanish version "coronavirus es ..." all of the top auto-completes were unfavorable. We found similar differences in multiple COVID-19 related terms, and hence we want to study these differences systematically over time,” said Singh.

Their research - which is some of the first to be conducted on algorithmic bias during a public health crisis - will help ensure equitable dissemination of health information.

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