Textual Analysis of Communications in COVID-19 Infected Community on Social Media

TL;DR

This paper analyzes COVID-19 related discussions on social media, revealing linguistic differences across topics and developing a classification model to categorize pandemic-related posts.

Contribution

It provides a linguistic analysis of COVID-19 discussions on social media and introduces a state-of-the-art classification model for categorizing pandemic-related posts.

Findings

Differences in psychological, emotional, and reasoning language across topics.

A classification model for pandemic-related social media posts.

Potential for social media analysis in pandemic research.

Abstract

During the COVID-19 pandemic, people started to discuss about pandemic-related topics on social media. On subreddit \textit{r/COVID19positive}, a number of topics are discussed or being shared, including experience of those who got a positive test result, stories of those who presumably got infected, and questions asked regarding the pandemic and the disease. In this study, we try to understand, from a linguistic perspective, the nature of discussions on the subreddit. We found differences in linguistic characteristics (e.g. psychological, emotional and reasoning) across three different categories of topics. We also classified posts into the different categories using SOTA pre-trained language models. Such classification model can be used for pandemic-related research on social media.