Our observations show that search engines retrieve inconsistent and sometime misleading results in relation to COVID-19, but it remains unclear what factors contribute to these information discrepancies and what principles each engine uses to construct hierarchies of knowledge. However, lack of transparency is particularly troublesome in times of emergency when the biases of filtering and ranking mechanisms become a matter of public health and national security. Criticism of algorithmic non-transparency in information distribution is not new (Pasquale, 2015 Kemper & Kolkman, 2019 Noble, 2018). The exact functioning of-and justification for-randomization and different source priorities is currently unknown. If we assume that a major driver of randomization is the maximization of user engagement by testing different ways of ranking search results and choosing the optimal hierarchy of information resources on a specific topic (e.g., the so-called “Google Dance” (Battelle, 2005)), then we would be in a situation in which companies’ private interests directly interfere with the people’s rights to access accurate and verifiable information. While randomization can encourage knowledge discovery by diversifying information acquired by individuals (Helberger, 2011), it can be detrimental when the society urgently needs to access consistent and accurate information – such as during a public health crisis. Then, in this scenario, access to reliable information is simply a matter of luck. Through randomization, a user sees what the search engine randomly decided that that specific user is allowed to see. Randomization ensures that what a user sees is not necessarily what the user chooses to see, and that different users are exposed to different information. We found that the degree of randomization varies between the engines: for some, such as Google and Bing, it mostly affects the composition of the “long tail” of search results, such as those below the top 10 results, while others, such as DuckDuckGo and Yandex, also randomize the top 10 results. The randomization of search results among users of the same search engine is of particular concern. For example, we found that some search algorithms potentially prioritize misleading sources of information, such as alternative media and social media content in the case of Yandex, while others prioritize authoritative sources (e.g., government-related pages), such as in the case of Google. ![]() Some differences in the results are expected given that search engines personalize their services (Hannak et al., 2013), but our study highlights that even non-personalized search results differ substantially. We identified large discrepancies in how different search engines disseminate information about the COVID-19 pandemic. ![]() Such discrepancies in search results can misinform the public and limit the rights of citizens to make decisions based on reliable and consistent information, which is of particular concern during an emergency, such as the COVID-19 pandemic.We also identified a considerable effect of randomization on how sources are ranked within the same search engine. ![]()
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