Open AccessUnderstanding the #longCOVID and #longhaulers Conversation on Twitter: Multimethod Study
Author(s) -
Sara Santarossa,
Ashley Redding,
Saily Sardinas,
Janine Hussein,
Alex Ramírez,
Andrea E. CassidyBushrow,
Philip Cheng,
Eunice Yu
Publication year - 2022
Publication title -
jmir infodemiology
Language(s) - English
Resource type - Journals
ISSN - 2564-1891
DOI - 10.2196/31259
Subject(s) - conversation , covid-19 , perception , psychology , social network (sociolinguistics) , social media , social network analysis , internet privacy , computer science , world wide web , communication , medicine , disease , pathology , neuroscience , infectious disease (medical specialty)
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.