COVID-19 Pandemic: Identifying Key Issues Using Social Media and Natural Language Processing

Area of Science:

• Social Sciences
• Computational Linguistics
• Public Health

Background:

• The COVID-19 pandemic significantly impacted global society.
• Social media platforms serve as a rich source for understanding public perception and experiences during crises.
• Identifying factors influencing pandemic control is crucial for effective public health strategies.

Purpose of the Study:

• To analyze public opinion on COVID-19 using social media data.
• To identify key themes and sentiments related to the pandemic from online discussions.
• To inform interventions by understanding public perceptions and challenges.

Main Methods:

• Natural Language Processing (NLP) techniques were applied to over 1 million COVID-19-related social media comments.
• Opinionated keyphrases and sentiment polarity (positive/negative) were extracted.
• Thematic analysis was used to categorize identified themes.

Main Results:

• A total of 34 negative themes were identified, with 17 related to economic, socio-political, educational, and political issues.
• Twenty positive themes reflecting public sentiment and experiences were also uncovered.
• Sentiment analysis provided a nuanced view of public discourse surrounding the pandemic.

Conclusions:

• Social media data offers valuable insights into public perceptions of the COVID-19 pandemic.
• Understanding negative themes, particularly socio-economic and political challenges, is essential for targeted interventions.
• Positive themes identified can guide the development of effective strategies to mitigate pandemic impacts.