Narratives of Gender and Death in Social Media in Times of COVID: Methodological Experiments Using Ethnoarchaeological Methods and Text Analytics

Abstract

The COVID pandemic has been a ‘natural experiment’ –albeit one replete with human suffering and unanticipated devastation. However, for social scientists, this period also offers a rare opportunity to observe how society contends with disaster and death. In the wake of the pandemic, as the ‘physical’ social worlds of people contracted through spatial curbs in the form of ‘social distancing’, ‘containment’ or ‘quarantine’, the digital media has become the most pervasive means of social exchange. We have used these social media exchanges as ethnographic accounts of human responses to death and despair in our paper. In the process we study how these narratives repeat certain well-known gender tropes. Our entry point is by noting that COVID is a ‘natural experiment’ (Diamond and Robinson, 2010), in the sense the evident behaviour can be established to be a causal response to pandemic without other confounding factors. This allows for an ‘objective’ application of ethnographic methods in interpreting the emerging narratives. Our second point is that ethnoarchaeology (David and Kramer, 2001), rather than ethnography, presents a more responsive toolbox for analysing the digital narratives –especially those around death and gender. We believe that ethnoarchaeology offers a methodological advantage when it comes to unravelling narratives around death and gender by using textual and material anchors. We posit that retweets and memes are curations, which showcase impersonalised registers of a dynamic ethnographic diary of reactions and responses to events around pandemic. These narratives reflect a society’s fears, anxieties, and visions. Additionally, we also look at memes/posts on statistical numbers around the pandemic as a separate narrative. We also look at patterns of co-occurrences and linkages as assemblages reflecting repeatedly changing rallying points around alternating nodes of hope (news on clinical trials and vaccine) and despair. We use advanced machine learning and text analytics methods to examine data from Twitter APIs using a set of key words related to COVID, death and gender. This is supplemented by survey of digital curations around COVID. Our application of this concept borrows from the works of quantitative ethnography and some recent works from contemporary archaeology (Perry and Band, 2020; Williams 2020), which use a combination of quantitative causal inference along with archaeology or ethnography. However, in our paper we differentiate ourselves by demonstrating the possibility of using foundations of ethnoarchaeology in conjunction with text analytics of digital media as a new methodological experiment.