Content analysis of Persian/Farsi Tweets during COVID-19 pandemic in Iran using NLP

TL;DR

This study analyzes over 530,000 Persian tweets during COVID-19 in Iran to identify key topics, public sentiment, and how discussions evolved, providing insights into Iranian social response to the pandemic using NLP techniques.

Contribution

It introduces a combined manual annotation and topic modeling framework to analyze large-scale Persian tweets, revealing dominant themes and public reactions during COVID-19.

Findings

Living under quarantine was a major topic.

Satire and news were the most common tweet types.

Public response evolved over time with specific trending topics.

Abstract

Iran, along with China, South Korea, and Italy was among the countries that were hit hard in the first wave of the COVID-19 spread. Twitter is one of the widely-used online platforms by Iranians inside and abroad for sharing their opinion, thoughts, and feelings about a wide range of issues. In this study, using more than 530,000 original tweets in Persian/Farsi on COVID-19, we analyzed the topics discussed among users, who are mainly Iranians, to gauge and track the response to the pandemic and how it evolved over time. We applied a combination of manual annotation of a random sample of tweets and topic modeling tools to classify the contents and frequency of each category of topics. We identified the top 25 topics among which living experience under home quarantine emerged as a major talking point. We additionally categorized broader content of tweets that shows satire, followed by news, is the dominant tweet type among the Iranian users. While this framework and methodology can be used to track public response to ongoing developments related to COVID-19, a generalization of this framework can become a useful framework to gauge Iranian public reaction to ongoing policy measures or events locally and internationally.