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 sou

Object Details

• Location: Deutsche Nationalbibliothek Frankfurt am Main
• Extent: Online-Ressource
• Language: Englisch
• Notes: Postprint ; begutachtet (peer reviewed) ; In: Social Science Computer Review (2021) OnlineFirst ; 1-17

Classification and Topics

• Classification: Bibliotheks- und Informationswissenschaft

Contributors, Places and Time

• Event: Veröffentlichung
• (where): Mannheim
• (who): SSOAR, GESIS – Leibniz-Institut für Sozialwissenschaften e.V.
• (when): 2021
• Creator: Urman, Aleksandra ; Makhortykh, Mykola ; Ulloa, Roberto

Further information

• DOI: 10.1177/08944393211006863
• URN: urn:nbn:de:0168-ssoar-75277-2
• Rights: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
• Last update: 15.08.2025, 7:28 AM CEST