The Matter of Chance: Auditing Web Search Results Related to the 2020 U.S. Presidential Primary Elections Across Six Search Engines

Abstract

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,” “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.

A story of (non)compliance, bias, and conspiracies: How Google and Yandex represented Smart Voting during the 2021 parliamentary elections in Russia

“…While we assume that Google and Yandex did not censor other queries related to Smart Voting because of not being explicitly requested to do so, it would be worthwhile to empirically check the validity of our assumption. Similarly, further research can examine whether there are cross-browser differences in retrieved results, which is a phenomenon observed by some earlier studies (e.g., Urman et al, 2021).…”

Laughing to forget or to remember? Anne Frank memes and mediatization of Holocaust memory

“…Consequently, while memes commonly appear on social media platforms (Lemish and Elias, 2020) and messengers (Sebba-Elran, 2021), we assume that search engines perform the role of content aggregators with the memes appearing in the top of search results likely being the ones which are more frequently engaged with across different platforms. Furthermore, considering that search engines serve as information gatekeepers in today’s digital ecologies (Urman et al, 2021), they can also be used by the users searching for memes (see, e.g. Wang and Wood, 2011).…”

“…We used multiple search engines because of the profound differences in content selection algorithms of individual search engines (Urman et al, 2021). Hence, to prevent potential bias in data selection we used five Western engines which are the most commonly used in Europe, in particular Spain (Statcounter, 2021b) and Switzerland (Statcounter, 2021a) where the authors are located, and two world’s largest non-Western engines (Yandex and Baidu).…”

Memory, counter-memory and denialism: How search engines circulate information about the Holodomor-related memory wars

“…Journalistic media and reference websites are also frequently prioritized sources in the case of non-historical queries (e.g. ones related to current politics; Urman et al, 2021).…”