Mining Greek Tweets on Long COVID Using Sentiment Analysis and Topic Modeling

Area of Science:

• Public Health
• Social Media Analysis
• Coronaviruses

Background:

• Long COVID affects 10-20% of patients post-COVID-19.
• Social media platforms are key for public discourse on health.
• Understanding public perception of Long COVID is crucial.

Purpose of the Study:

• Analyze Greek Twitter data from 2022 on Long COVID.
• Identify prevalent discussion topics and public sentiment.
• Inform public health strategies through social media insights.

Main Methods:

• Collected and analyzed Greek-language tweets from 2022.
• Utilized natural language processing for topic extraction.
• Performed sentiment analysis on user-generated content.

Main Results:

• Key topics include Long COVID effects, healing time, impact on children, and vaccines.
• 59% of analyzed tweets expressed negative sentiment towards Long COVID.
• Positive or neutral sentiment comprised the remaining 41%.

Conclusions:

• Greek social media reveals significant negative sentiment regarding Long COVID.
• Public health bodies can leverage social media for disease perception monitoring.
• Proactive analysis of online discussions can guide public health interventions.