THE DISINFECTANT DIVERSION: FRAMING STRATEGIES OF PARTISAN MEDIA IN INTERPRETING THE COVID-19 PANDEMIC
Keywords:Framing, narratives, storytelling, partisan news, news media
AbstractFollowing the rise of Donald Trump leading up to the 2016 US presidential election, political communication scholars have turned a critical eye towards the role of conservative media outlets in the construction of an overarching meta-narrative, largely referred to in the existing literature as the “deep story” (Hochschild, 2016). The aim of the present study to extend this seminal work to analyze how mainstream, conservative, and liberal outlets rely on meta-narratives to construct meaning in their coverage of the COVID-19 pandemic. Employing qualitative methods, we analyze the coverage that six American news outlets afforded the April 23rd 2020 Coronavirus Task Force news briefing, where President Trump insinuated injections of disinfectant could be a useful way to fight COVID-19. Our analysis includes 115 news articles, 41 Facebook posts and 87 television clips from these outlets. Our results reveal that both the left and right wing media systems employed overarching narratives in their coverage. The left-wing media heavily emphasized the tendency to deny or argue scientific fact among conservatives. In contrast, we observed that the right-wing media constantly used similar framing strategies in an attempt to vilify the left-wing media and liberals. Considering the existing literature (Kreiss, 2018: Poletta & Callahan, 2019), we observed many instances where right-wing pundits and journalists relied on previously established heuristics, cuing audiences to perceive the left-wing media as elitist out to discredit Trump. Our findings provide an in-depth analysis of how partisan media relies on meta-narratives to convey meaning to their audiences.
How to Cite
Trifiro, B., Wells, C., & Rochefort, A. (2021). THE DISINFECTANT DIVERSION: FRAMING STRATEGIES OF PARTISAN MEDIA IN INTERPRETING THE COVID-19 PANDEMIC. AoIR Selected Papers of Internet Research, 2021. https://doi.org/10.5210/spir.v2021i0.12253