  We examine how six search engines filter and rank information in relation to
the queries on the U.S. 2020 presidential primary elections under the default -
that is nonpersonalized - conditions. For that, we utilize an algorithmic
auditing methodology that uses virtual agents to conduct large-scale analysis
of algorithmic information curation in a controlled environment. Specifically,
we look at the text search results for "us elections", "donald trump", "joe
biden" and "bernie sanders" queries on Google, Baidu, Bing, DuckDuckGo, Yahoo,
and Yandex, during the 2020 primaries. Our findings indicate substantial
differences in the search results between search engines and multiple
discrepancies within the results generated for different agents using the same
search engine. It highlights that whether users see certain information is
decided by chance due to the inherent randomization of search results. We also
find that some search engines prioritize different categories of information
sources with respect to specific candidates. These observations demonstrate
that algorithmic curation of political information can create information
inequalities between the search engine users even under nonpersonalized
conditions. Such inequalities are particularly troubling considering that
search results are highly trusted by the public and can shift the opinions of
undecided voters as demonstrated by previous research.
