The ‘Screening Protest’ project compares (tele)visual narratives of dissent across time, space, media culture and genre in three component studies. The first explores how representations of protest in global television news between 2008 and 2018 vary according to protest site and issue, and newsroom culture. The second investigates depcitions of protest at selected moments in the last century, to gain insights into how political and social change intersect with developments in media ecology. The third is about popular cultural portrayals of the protester – in films and tv series such as Spartacus, Robin Hood, The Hunger Games and Mr. Robot. Combining insights from political communication and media studies, the project provides an unusually rich empirical perspective on the problem of representation in changing media landscapes. Visual analytics are used to present the complexity of the quantitative data gathered for comparisons across global new channels’ coverage of contemporary events, including protest.
We are surrounded by screens, and have them perpetually at our fingertips. Why study television? Because it is more than a version of the screen. It is a medium that has stayed put in the living room, despite other devices having moved into the home. It is content that can be accessed on that living room set, but also online, on YouTube and on the phone. Television drama awards now go to series made and distributed by streaming services like Netlix. Television, in other words, is both ‘old’ and ‘new’ media.
In this project, we study national television and television drama, but pride of place goes to the global television news channels whose output we monitor on a daily basis: Al Jazeera English, BBC World, CNN International, China’s CGTN (formerly CCTV), Deutsche Welle, Euronews, and RT (formerly known as Russia Today). Despite being under-researched, these channels are of interest because they comprise a spectrum of financing solutions and relations to political power, including an MNC-owned newsroom, a commercially-funded channel moored in the public service tradition of a democratic state, one that is financed by the ruling dynasty of a monarchy in which political parties are not permitted, and one bankrolled by an authoritarian government.
We are interested in both everyday reporting and the deeper structures of meaning to be found in televisual representations of protest. The project thus combines large-scale quantitative mapping with closer analysis of the protest narratives.
Every day, we code the headlines of last night’s broadcasts, summarizing the news, noting the countries involved, whether the news concerns a global issue and – most importantly – whether protest is involved. This gives us a measure of how ‘protestful’ the newsworlds of the channels are.
The next step takes a closer look at the full news reports that go with the protest headlines. We code for issue (are protesters calling for regime change, against a war, or concerned about the enviroment, or something else?). We ask who gets a speaking part (political elite, protester or ordinary person? man or woman? white or not?) and note other things such as protester appearance and the use of amateur footage.
The first two steps are sort of like the first shaft in the archaeological excavation of a mound. At the third stage – of narrative analysis – the coding work resembles what the archaeologist does with the sieve and fine brush. Together, the superficial mapping and deeper reading lay bare the structures of mediated strategic narratives.
The development of coding categories and routines has been a group effort. The coding team is comprised of people of 11 nationalities and different backgrounds. We take turns analyzing different channels and periods, to facilitate comparability, and to avoid becoming exclusively immersed in one newsworld. The raw data recorded in a database designed specially for the project is subsequently worked through in SPSS. See the Results page for a selection of our findings, and Outcomes for what we have written up and presented.