This paper is available on arxiv under CC 4.0 license.
Authors:
(1) Mykola Makhortykh, Institute of Communication and Media Studies, University of Bern;
(2) Aleksandra Urman, Social Computing Group, University of Zurich;
(3) Roberto Ulloa, GESIS – Leibniz-Institute for the Social Sciences. Table of Links Author Note Abstract Theoretical background Methodology Findings Conclusion and References Abstract In today’s high-choice media environment, search engines play an integral role in informing individuals and societies about the latest events. The importance of search algorithms is even higher at the time of crisis, when users search for information to understand the causes and the consequences of the current situation and decide on their course of action. In our paper, we conduct a comparative audit of how different search engines prioritize visual information related to COVID-19 and what consequences it has for the representation of the pandemic. Using a virtual agent-based audit approach, we examine image search results for the term “coronavirus” in English, Russian and Chinese on five major search engines: Google, Yandex, Bing, Yahoo, and DuckDuckGo. Specifically, we focus on how image search results relate to generic news frames (e.g., the attribution of responsibility, human interest, and economics) used in relation to COVID-19 and how their visual composition varies between the search engines. Keywords: search engine, algorithm audit, COVID, framing, news frames, Google, image search, visual communication In today’s “high-choice” (Van Aelst et al. 2017) media environment, search engines such as Google play an integral role in informing individuals about important societal developments. Together with social media and news aggregators, search engines increasingly become a major pathway to finding and engaging with the latest news (Newman et al. 2019) and historical information used to contextualize these recent developments (Zavadski and Toepfl 2019; Makhortykh, Urman and Ulloa 2021). Using complex algorithms, search engines filter and rank sources to counter the information overload and supply their users with the most relevant content. By prioritizing certain sources and judging how relevant a particular piece of information is, search engines serve as informationgatekeepers (Laidlaw 2010) that structure individual and collective knowledge about the subjects varying from elections (Trielli and Diakopoulos 2022) to diseases (Paramita et al. 2021) to the matters of gender and race (Urman, Makhortykh and Ulloa 2022). The gatekeeping functions of search engines translate into their substantial influence on different aspects of contemporary societies, in particular the ones related to information literacy (van Dijk 2010). This influence varies from search engines’ information ranking affecting undecided voter preferences (Epstein and Robertson 2015) to their algorithmically constructed knowledge hierarchies reinforcing gender and racial stereotypes (Noble 2018; Urman and Makhortykh 2022). The functionality of search engines becomes particularly important at times of crises, when the need to create new hierarchies of knowledge in the face of uncertainty often leads to a state of “epistemic instability” (Harambam 2020) where the authoritative sources of truth are challenged, and alternative interpretations thrive. Under such conditions, filtering and ranking mechanisms used by search engines play an integral role in determining how individuals and societies understand the causes and the consequences of the current situation and decide on their course of action. In this chapter, we look at how the above-mentioned complexities of information distribution via search engine algorithms affect pandemic literacy during the COVID-19 crisis. Understanding pandemic literacy as the ability to locate and effectively use information related to health threats associated with widespread epidemic diseases, we examine how search engines can facilitate, but also impede the ability of individuals and societies to stay informed about the COVID-19 pandemic. Specifically, we investigate the visual aspect of pandemic literacy by looking at visual representations of COVID-19 constructed by five major search engines in February 2020. Our interest in COVID-19 is characterized by the profound epistemic crisis experienced in relation to the pandemic and amplified by the intense use of digital media for disseminating false information about the disease (Allem 2020). By scrutinizing how search engines represent the pandemic, we strive to advance the understanding of their role at the time of epistemic instability and how search engines construct knowledge hierarchies in relation to a new and highly contradictory phenomenon. Our decision to focus on the visual representation of COVID-19 by search engines as contrasted to its textual representation that was examined in earlier studies (e.g. Makhortykh, Urman, and Ulloa 2020; Toepfl, Kravets, Ryzhova and Beseler 2022) is attributed to images as an effective means of communicating complex phenomena, in particular those that are hard to express verbally (Bleiker 2018). Images also stir strong emotional responses that make them a potent catalyst of societal mobilization but also result in their frequent abuse for manipulating public opinion (Ruijgrok 2017). To investigate how search engines represent and interpret COVID-19 visually, we combined qualitative analysis of visual frames—i.e., consistent patterns of selecting some aspects of the perceived reality and making them more salient (Entman 1993)—with quantitative techniques of algorithmic auditing used to investigate the functionality and impact of “decision-making algorithms” (Mittelstadt 2016). Utilizing a novel method of algorithmic auditing to extract visual search results, we compared the use of generic news frames (e.g., the attribution of responsibility, human interest, and economics; Semetko and Valkenburg 2000) for framing COVID-19 on five search engines: Google, Bing, Yahoo, Yandex and DuckDuckGo in English, Russian, and Mandarin Chinese. While doing so, we specifically investigated the following three research questions: How different/similar is the representation of COVID-19 via search engines compared with framing of earlier pandemics in legacy media? What types of news frames were prevalent in the representation and interpretation of COVID- 19? And are there substantial differences in the representation of COVID-19 among different search engines and languages? This paper is available on Arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. Authors: (1) Mykola Makhortykh, Institute of Communication and Media Studies, University of Bern; (2) Aleksandra Urman, Social Computing Group, University of Zurich; (3) Roberto Ulloa, GESIS – Leibniz-Institute for the Social Sciences. This paper is available on arxiv under CC 4.0 license. Authors: (1) Mykola Makhortykh, Institute of Communication and Media Studies, University of Bern; (2) Aleksandra Urman, Social Computing Group, University of Zurich; (3) Roberto Ulloa, GESIS – Leibniz-Institute for the Social Sciences. Table of Links Author Note Author Note Abstract Abstract Theoretical background Theoretical background Methodology Methodology Findings Findings Conclusion and References Conclusion and References Abstract In today’s high-choice media environment, search engines play an integral role in informing individuals and societies about the latest events. The importance of search algorithms is even higher at the time of crisis, when users search for information to understand the causes and the consequences of the current situation and decide on their course of action. In our paper, we conduct a comparative audit of how different search engines prioritize visual information related to COVID-19 and what consequences it has for the representation of the pandemic. Using a virtual agent-based audit approach, we examine image search results for the term “coronavirus” in English, Russian and Chinese on five major search engines: Google, Yandex, Bing, Yahoo, and DuckDuckGo. Specifically, we focus on how image search results relate to generic news frames (e.g., the attribution of responsibility, human interest, and economics) used in relation to COVID-19 and how their visual composition varies between the search engines. Keywords: search engine, algorithm audit, COVID, framing, news frames, Google, image search, visual communication In today’s “high-choice” (Van Aelst et al. 2017) media environment, search engines such as Google play an integral role in informing individuals about important societal developments. Together with social media and news aggregators, search engines increasingly become a major pathway to finding and engaging with the latest news (Newman et al. 2019) and historical information used to contextualize these recent developments (Zavadski and Toepfl 2019; Makhortykh, Urman and Ulloa 2021). Using complex algorithms, search engines filter and rank sources to counter the information overload and supply their users with the most relevant content. By prioritizing certain sources and judging how relevant a particular piece of information is, search engines serve as informationgatekeepers (Laidlaw 2010) that structure individual and collective knowledge about the subjects varying from elections (Trielli and Diakopoulos 2022) to diseases (Paramita et al. 2021) to the matters of gender and race (Urman, Makhortykh and Ulloa 2022). The gatekeeping functions of search engines translate into their substantial influence on different aspects of contemporary societies, in particular the ones related to information literacy (van Dijk 2010). This influence varies from search engines’ information ranking affecting undecided voter preferences (Epstein and Robertson 2015) to their algorithmically constructed knowledge hierarchies reinforcing gender and racial stereotypes (Noble 2018; Urman and Makhortykh 2022). The functionality of search engines becomes particularly important at times of crises, when the need to create new hierarchies of knowledge in the face of uncertainty often leads to a state of “epistemic instability” (Harambam 2020) where the authoritative sources of truth are challenged, and alternative interpretations thrive. Under such conditions, filtering and ranking mechanisms used by search engines play an integral role in determining how individuals and societies understand the causes and the consequences of the current situation and decide on their course of action. In this chapter, we look at how the above-mentioned complexities of information distribution via search engine algorithms affect pandemic literacy during the COVID-19 crisis. Understanding pandemic literacy as the ability to locate and effectively use information related to health threats associated with widespread epidemic diseases, we examine how search engines can facilitate, but also impede the ability of individuals and societies to stay informed about the COVID-19 pandemic. Specifically, we investigate the visual aspect of pandemic literacy by looking at visual representations of COVID-19 constructed by five major search engines in February 2020. Our interest in COVID-19 is characterized by the profound epistemic crisis experienced in relation to the pandemic and amplified by the intense use of digital media for disseminating false information about the disease (Allem 2020). By scrutinizing how search engines represent the pandemic, we strive to advance the understanding of their role at the time of epistemic instability and how search engines construct knowledge hierarchies in relation to a new and highly contradictory phenomenon. Our decision to focus on the visual representation of COVID-19 by search engines as contrasted to its textual representation that was examined in earlier studies (e.g. Makhortykh, Urman, and Ulloa 2020; Toepfl, Kravets, Ryzhova and Beseler 2022) is attributed to images as an effective means of communicating complex phenomena, in particular those that are hard to express verbally (Bleiker 2018). Images also stir strong emotional responses that make them a potent catalyst of societal mobilization but also result in their frequent abuse for manipulating public opinion (Ruijgrok 2017). To investigate how search engines represent and interpret COVID-19 visually, we combined qualitative analysis of visual frames—i.e., consistent patterns of selecting some aspects of the perceived reality and making them more salient (Entman 1993)—with quantitative techniques of algorithmic auditing used to investigate the functionality and impact of “decision-making algorithms” (Mittelstadt 2016). Utilizing a novel method of algorithmic auditing to extract visual search results, we compared the use of generic news frames (e.g., the attribution of responsibility, human interest, and economics; Semetko and Valkenburg 2000) for framing COVID-19 on five search engines: Google, Bing, Yahoo, Yandex and DuckDuckGo in English, Russian, and Mandarin Chinese. While doing so, we specifically investigated the following three research questions: How different/similar is the representation of COVID-19 via search engines compared with framing of earlier pandemics in legacy media? What types of news frames were prevalent in the representation and interpretation of COVID- 19? And are there substantial differences in the representation of COVID-19 among different search engines and languages? This paper is available on Arxiv under CC 4.0 license. Arxiv Arxiv

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