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. 2022 Feb 22;2(1):e31259.
doi: 10.2196/31259. eCollection 2022 Jan-Jun.

Understanding the #longCOVID and #longhaulers Conversation on Twitter: Multimethod Study

Sara Santarossa  1 , Ashley Rapp  1 , Saily Sardinas  1 , Janine Hussein  1 , Alex Ramirez  1   2 , Andrea E Cassidy-Bushrow  1 , Philip Cheng  3 , Eunice Yu  4
Affiliations

Understanding the #longCOVID and #longhaulers Conversation on Twitter: Multimethod Study

Sara Santarossa et al. JMIR Infodemiology. .

Abstract

Background: The scientific community is just beginning to uncover the potential long-term effects of COVID-19, and one way to start gathering information is by examining the present discourse on the topic. The conversation about long COVID-19 on Twitter provides insight into related public perception and personal experiences.

Objective: The aim of this study was to investigate the #longCOVID and #longhaulers conversations on Twitter by examining the combined effects of topic discussion and social network analysis for discovery on long COVID-19.

Methods: A multipronged approach was used to analyze data (N=2500 records from Twitter) about long COVID-19 and from people experiencing long COVID-19. A text analysis was performed by both human coders and Netlytic, a cloud-based text and social networks analyzer. The social network analysis generated Name and Chain networks that showed connections and interactions between Twitter users.

Results: Among the 2010 tweets about long COVID-19 and 490 tweets by COVID-19 long haulers, 30,923 and 7817 unique words were found, respectively. For both conversation types, "#longcovid" and "covid" were the most frequently mentioned words; however, through visually inspecting the data, words relevant to having long COVID-19 (ie, symptoms, fatigue, pain) were more prominent in tweets by COVID-19 long haulers. When discussing long COVID-19, the most prominent frames were "support" (1090/1931, 56.45%) and "research" (435/1931, 22.53%). In COVID-19 long haulers conversations, "symptoms" (297/483, 61.5%) and "building a community" (152/483, 31.5%) were the most prominent frames. The social network analysis revealed that for both tweets about long COVID-19 and tweets by COVID-19 long haulers, networks are highly decentralized, fragmented, and loosely connected.

Conclusions: This study provides a glimpse into the ways long COVID-19 is framed by social network users. Understanding these perspectives may help generate future patient-centered research questions.

Keywords: COVID-19; PASC; Twitter; communication; experience; insight; long term; patient-centered; patient-centered care; perception; postacute sequela of COVID-19; social media; social network analysis; symptom.

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Conflict of interest statement

Conflicts of Interest: None declared.

Figures

Figure 1
Figure 1
Word cloud of tweets about long COVID-19 (left) and tweets by COVID-19 long haulers (right) based on number of instances from a one-time Netlytic data pull in February of 2021.
Figure 2
Figure 2
Name (left) and Chain (right) networks for tweets about long COVID-19 conversations on Twitter from a one-time Netlytic data pull in February of 2021, presented using a Dr L layout [30].
Figure 3
Figure 3
Name (left) and Chain (right) networks for tweets by COVID-19 long haulers based on conversations on Twitter from a one-time Netlytic data pull in February of 2021, presented using a Fruchterman-Reingold layout [31].
See this image and copyright information in PMC

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