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

Area of Science:

• Public Health
• Social Media Analysis
• Infectious Diseases

Background:

• The scientific community is actively investigating the long-term effects of COVID-19.
• Analyzing online discourse, particularly on Twitter, offers insights into public perception and personal experiences of long COVID-19.

Purpose of the Study:

• To investigate Twitter conversations related to #longCOVID and #longhaulers.
• To examine the combined effects of topic discussion and social network analysis for understanding long COVID-19.

Main Methods:

• A multipronged approach analyzed 2500 Twitter records concerning long COVID-19.
• Text analysis was conducted using human coders and Netlytic, a social network analyzer.
• Social network analysis generated Name and Chain networks to visualize user interactions.

Main Results:

• Analysis of 2010 #longCOVID and 490 #longhaulers tweets revealed distinct word frequencies.
• Prominent frames in long COVID-19 discussions were 'support' (56.45%) and 'research' (22.53%).
• Long hauler conversations highlighted 'symptoms' (61.5%) and 'building a community' (31.5%).
• Social network analysis indicated highly decentralized and fragmented networks for both conversation types.

Conclusions:

• This study offers insights into how social media users frame long COVID-19.
• Understanding these perspectives can inform future patient-centered research on long COVID-19.