Examining bias perpetuation in academic search engines: an algorithm audit of Google and Semantic Scholar.
CoRR(2023)
摘要
Researchers rely on academic web search engines to find scientific sources,
but search engine mechanisms may selectively present content that aligns with
biases embedded in the queries. This study examines whether confirmation-biased
queries prompted into Google Scholar and Semantic Scholar will yield skewed
results. Six queries (topics across health and technology domains such as
"vaccines" or "internet use") were analyzed for disparities in search results.
We confirm that biased queries (targeting "benefits" or "risks") affect search
results in line with the bias, with technology-related queries displaying more
significant disparities. Overall, Semantic Scholar exhibited fewer disparities
than Google Scholar. Topics rated as more polarizing did not consistently show
more skewed results. Academic search results that perpetuate confirmation bias
have strong implications for both researchers and citizens searching for
evidence. More research is needed to explore how scientific inquiry and
academic search engines interact.
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