Conclusion and References

Table of Links

Author Note

Abstract

Theoretical background

Methodology

Findings

Conclusion and References

Conclusions

Traditionally, the process of framing is constituted by competition between actors, who compete “to define a problem, assign blame, and suggest who is responsible for addressing it” (Kee, Faridah, and Normah 2010, 107). However, in a high-choice media environment, where the audience frequently struggles with the information overload, this competition is increasingly affected by the algorithms (Entman and Usher 2018) that construct new hierarchies of knowledge and rank information sources. The importance of these new framing actors assumes particularly high importance under a state of epistemic instability, similar to that observed during the COVID-19 pandemic, where societies and individuals have to deal with the large volumes of (digital)

information coming from different and often mutually contradictory sources.

Our findings indicate that visual images retrieved by the search engine algorithms do not necessarily have to do much with news frames in their traditional sense. While some of the search return results can be related to specific types of generic media frames (in particular, for Chinese and Russian language messages), in many cases these results are constituted by schematic images that do not necessarily provide a clear interpretation of the issue in question. These observations throw into question not only the framing potential of search engines but also their potential for increasing pandemic literacy, in particular in a time of emergency, where access to (visual) information can be important for individual and collective well-being.

In cases where search engines return news frames in relation to COVID-19, we find the human interest frame prevalent despite the more frequent use of the responsibility frame in earlier pandemics in legacy media (Kee, Faridah, and Normah 2010). The prevalence of the former frame can be attributed to the importance of bringing “a human face” (Semetko and Valkenburg 2000) to the representation of the disease and its consequences. The higher visibility of the human-interest frame can be viewed as a positive feature of framing COVID-19 as it can stress the severity of the emergency and importance of following healthcare prescriptions. At the same time, this specific frame can be argued to have the least interpretative value among the generic frames, instead appealing to audience emotions that might also enable the manipulation of public opinion.

Finally, we observed multiple cross-language and cross-engine discrepancies in the visual framing of COVID-19. These discrepancies do, to a certain degree, align with the “filter bubble” (Pariser 2011) argument by leading to rather different interpretations of the pandemic depending on the engine utilized by the users and the language of their search queries. Specifically, the probability for being exposed to the human interest frame in the context of COVID-19 (and, thus, being more aware of its severity and potential human toll) at the period when we conducted our study was higher for the queries conducted in Chinese, whereas the most common content for English queries was constituted by the schematic depictions of coronavirus.

It is important to note some limitations of the study. Because of our interest in the applicability of generic news frames to search engine-based framing, we used pre-established frame categories, not ones established via inductive coding as some earlier studies did. Our deductive coding could potentially limit our data interpretations, so in future studies we will go beyond the five generic frames we looked at currently. Another limitation is that the current study is based on a snapshot experiment conducted at the specific point of the COVID-19 pandemic. A more longitudinal approach is required to examine consistency of observed differences among the search engines and also changes in the framing of the pandemic through time. Additionally, it would be beneficial to rely on the broader selection of search queries and, possibly, use crowdsourcing for identifying what search combinations are the most used by the individuals searching for information about COVID similar to how it was done by Paramita et al. (2021). Yet, even with these limitations, our study raises important points about the role of search engine algorithms in the process of framing and their possible influence on the way the public is informed at the time of healthcare emergencies.

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