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

arXiv: https://arxiv.org/abs/2105.01189 · HTML: https://mlsanity.com/paper/2105.01189/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 13 references that are in the map.
- First posted on arXiv 3 May 2021.

## Builds on (most cited first, 5 of 5)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://mlsanity.com/paper/1906.08237/) (2019)
- [A Comparison of LSTM and BERT for Small Corpus](https://mlsanity.com/paper/2009.05451/) (2020)
- [What Contributes to a Crowdfunding Campaign's Success? Evidence and Analyses from GoFundMe Data](https://mlsanity.com/paper/2001.05446/) (2020)
- [In the Eyes of the Beholder: Analyzing Social Media Use of Neutral and Controversial Terms for COVID-19](https://mlsanity.com/paper/2004.10225/) (2020)

## Abstract (arXiv)

During the COVID-19 pandemic, people started to discuss about pandemic-related topics on social media. On subreddit textitr/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.