TRAC: COVID - TRust And Communication: a Coronavirus Online VIsual Dashboard

The COVID-19 crisis has required mass communication and public understanding on an unprecedented scale. During this time there has been a proliferation of online discussion, news sharing and emergence of 'information sources' concerning COVID-19. Such proliferation has raised concerns about the potential dangers of dis/misinformation. As yet, however, neither the extent of the issue nor the sources of information have been studied in detail. Furthermore, despite communication being at the heart of COVID-19 public health efforts, there has been a surprising lack of input from linguistic experts. This project aims to build a large-scale dataset of Twitter posts, which will be made available via an open-access online dashboard incorporating intuitive visualisations. The dataset will be novel in capturing not just the content of tweets, but also the content of web-pages shared in the tweets. Drawing on automated corpus linguistic methods and social network analysis, the dashboard will uncover the multi-layered content of shared information (original links, tweets, replies, retweets), alongside a deeper understanding of the online networks through which (mis)information is shared. To demonstrate the applicability of our novel approach to a wide range of stakeholders, the methodology and dashboard will be validated through two case studies, each focussing on a potentially dangerous area of miscommunication relating to COVID-19. These case studies will approach the problem from a linguistic perspective, examining the clarity and reception of official messaging and the trustworthiness of information sources.