Framing COVID-19: How we conceptualize and discuss the pandemic on Twitter

Abstract

Doctors and nurses in these weeks are busy in the trenches, fighting against a new invisible enemy: Covid-19. Cities are locked down and civilians are besieged in their own homes, to prevent the spreading of the virus. War-related terminology is commonly used to frame the discourse around epidemics and diseases. Arguably the discourse around the current epidemic will make use of war-related metaphors too,not only in public discourse and the media, but also in the tweets written by non-experts of mass communication. We hereby present an analysis of the discourse around #Covid-19, based on a corpus of 200k tweets posted on Twitter during March and April 2020. Using topic modelling we first analyze the topics around which the discourse can be classified. Then, we show that the WAR framing is used to talk about specific topics, such as the virus treatment, but not others, such as the effects of social distancing on the population. We then measure and compare the popularity of the WAR frame to three alternative figurative frames (MONSTER, STORM and TSUNAMI) and a literal frame used as control (FAMILY). The results show that while the FAMILY literal frame covers a wider portion of the corpus, among the figurative framings WAR is the most frequently used, and thus arguably the most conventional one. However, we conclude, this frame is not apt to elaborate the discourse around many aspects involved in the current situation. Therefore, we conclude, in line with previous suggestions, a plethora of framing options, or a metaphor menu, may facilitate the communication of various aspects involved in the Covid-19-related discourse on the social media, and thus support civilians in the expression of their feelings, opinions and ideas during the current pandemic.

arXiv comment41 pages, 6 figures

References in corpus (2)

• Distributed Representations of Sentences and Documents
• Combining Search, Social Media, and Traditional Data Sources to Improve Influenza Surveillance

Cited by in corpus (8)

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• Classification Aware Neural Topic Model and its Application on a New COVID-19 Disinformation Corpus
• Causal Modeling of Twitter Activity During COVID-19
• Large-scale, Language-agnostic Discourse Classification of Tweets During COVID-19
• Symptom extraction from the narratives of personal experiences with COVID-19 on Reddit
• Studying Moral-based Differences in the Framing of Political Tweets
• Human Interest or Conflict? Leveraging LLMs for Automated Framing Analysis in TV Shows