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. 2020 Sep 30;15(9):e0240010.
doi: 10.1371/journal.pone.0240010. eCollection 2020.

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

Philipp Wicke  1 , Marianna M Bolognesi  2
Affiliations

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

Philipp Wicke et al. PLoS One. .

Erratum in

Abstract

Doctors and nurses in these weeks and months 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. The discourse around the current epidemic makes use of war-related metaphors too, not only in public discourse and in 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 large corpus 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 frame covers a wider portion of the corpus, among the figurative frames WAR, a highly conventional one, is the frame used most frequently. Yet, this frame does not seem to be apt to elaborate the discourse around some 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 beliefs during the current pandemic.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Word cloud of the most common words in the corpus of over 200k collected tweets with at least one hashtag relating to the covid19 epidemic.
Fig 2
Fig 2. Word clouds form N = 4 LDA topic modeling with greater words signaling greater significance.
Fig 3
Fig 3. Depiction of the word clouds for each of the 16 topics clustered by the LDA.
Fig 4
Fig 4. LDA-predicted average probability of WAR term contributing to one of 4 topics.
Fig 5
Fig 5. LDA-predicted average probability of a WAR term contributing to one of 16 topics.
Fig 6
Fig 6. Five histograms depicting the occurrences of terms for each frame within the corpus.
Fig 7
Fig 7. Comparison of the two corpora for five frames and two time spans (2 weeks, 2 months).
Each bar indicates the percentage of a frame within the respective corpus.
See this image and copyright information in PMC

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