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

TL;DR

This study uses NLP to analyze over 1 million social media comments related to COVID-19, identifying key themes and sentiments to understand public perceptions and challenges during the pandemic.

Contribution

It introduces a large-scale NLP-based thematic analysis of social media data to uncover public concerns and positive aspects related to COVID-19.

Findings

Identified 34 negative themes, including economic and political issues.

Found 20 positive themes reflecting supportive perceptions.

Provided insights for targeted interventions based on social media sentiments.

Abstract

The COVID-19 pandemic has affected people's lives in many ways. Social media data can reveal public perceptions and experience with respect to the pandemic, and also reveal factors that hamper or support efforts to curb global spread of the disease. In this paper, we analyzed COVID-19-related comments collected from six social media platforms using Natural Language Processing (NLP) techniques. We identified relevant opinionated keyphrases and their respective sentiment polarity (negative or positive) from over 1 million randomly selected comments, and then categorized them into broader themes using thematic analysis. Our results uncover 34 negative themes out of which 17 are economic, socio-political, educational, and political issues. 20 positive themes were also identified. We discuss the negative issues and suggest interventions to tackle them based on the positive themes and research evidence.