JMIR Publications

ABSTRACT

Background:

The scientific community is just beginning to uncover potential long-term effects of COVID-19, and one way to start gathering information is by examining the present discourse on the topic.

Objective:

The conversation about long COVID-19 on Twitter provides insight into related public perception and personal experiences.

Methods:

A multipronged approach was used to analyze data (N = 2,500 records from Twitter) about long-COVID and from people experiencing long COVID-19. A text analysis was completed by both human coders and Netlytic, a cloud-based text and social networks analyzer. A social network analysis generated Name and Chain networks that showed connections and interactions between Twitter users.

Results:

Among the 2,010 tweets about long COVID-19, and 490 tweets by COVID-19 long-haulers 30,923 and 7,817 unique words were found, respectively. For booth conversation types ‘#longcovid’ and ‘covid’ were the most frequently mentioned words, however, through visually inspecting the data, words relevant to having long COVID-19 (i.e., 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; 56.45%) and ‘research’ (435; 21.65%). In COVID-19 long haulers conversations, ‘symptoms’ (297; 61.5%) and ‘building a community’ (152; 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:

The present 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.