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A Large-Scale Analysis of Persian Tweets Regarding Covid-19 Vaccination

Taha ShabaniMirzaei, Houmaan Chamani, Amirhossein Abaskohi, Zhivar Sourati Hassan Zadeh, Behnam Bahrak

TL;DR

The study analyzes Persian-language Twitter data to understand public opinion on Covid-19 vaccination in Iran through a comprehensive pipeline that combines keyword-driven data extraction, topic modeling (LDA vs GSDMM), transformer-based tweet classification (with ParsBERT as the leading model), emotion analysis using a Persian hedonomics lexicon, policy-relevant themes through grounded theory, and user interaction networks before and after vaccine rollout. It delivers a large, end-to-end framework for low-resource languages that yields 1.04 million vaccine-related tweets, 31 identified vaccine themes, and insight into how emotions and influencer dynamics evolved during the vaccination period. The work demonstrates that online discourse shifted toward greater acceptance over time, with clear correlations between vaccination milestones and emotion trends, offering actionable guidance for health communication and misinformation countermeasures in Iran. It also provides a robust methodological blueprint for similar studies in other low-resource languages and social media platforms.

Abstract

The Covid-19 pandemic had an enormous effect on our lives, especially on people's interactions. By introducing Covid-19 vaccines, both positive and negative opinions were raised over the subject of taking vaccines or not. In this paper, using data gathered from Twitter, including tweets and user profiles, we offer a comprehensive analysis of public opinion in Iran about the Coronavirus vaccines. For this purpose, we applied a search query technique combined with a topic modeling approach to extract vaccine-related tweets. We utilized transformer-based models to classify the content of the tweets and extract themes revolving around vaccination. We also conducted an emotion analysis to evaluate the public happiness and anger around this topic. Our results demonstrate that Covid-19 vaccination has attracted considerable attention from different angles, such as governmental issues, safety or hesitancy, and side effects. Moreover, Coronavirus-relevant phenomena like public vaccination and the rate of infection deeply impacted public emotional status and users' interactions.

A Large-Scale Analysis of Persian Tweets Regarding Covid-19 Vaccination

TL;DR

The study analyzes Persian-language Twitter data to understand public opinion on Covid-19 vaccination in Iran through a comprehensive pipeline that combines keyword-driven data extraction, topic modeling (LDA vs GSDMM), transformer-based tweet classification (with ParsBERT as the leading model), emotion analysis using a Persian hedonomics lexicon, policy-relevant themes through grounded theory, and user interaction networks before and after vaccine rollout. It delivers a large, end-to-end framework for low-resource languages that yields 1.04 million vaccine-related tweets, 31 identified vaccine themes, and insight into how emotions and influencer dynamics evolved during the vaccination period. The work demonstrates that online discourse shifted toward greater acceptance over time, with clear correlations between vaccination milestones and emotion trends, offering actionable guidance for health communication and misinformation countermeasures in Iran. It also provides a robust methodological blueprint for similar studies in other low-resource languages and social media platforms.

Abstract

The Covid-19 pandemic had an enormous effect on our lives, especially on people's interactions. By introducing Covid-19 vaccines, both positive and negative opinions were raised over the subject of taking vaccines or not. In this paper, using data gathered from Twitter, including tweets and user profiles, we offer a comprehensive analysis of public opinion in Iran about the Coronavirus vaccines. For this purpose, we applied a search query technique combined with a topic modeling approach to extract vaccine-related tweets. We utilized transformer-based models to classify the content of the tweets and extract themes revolving around vaccination. We also conducted an emotion analysis to evaluate the public happiness and anger around this topic. Our results demonstrate that Covid-19 vaccination has attracted considerable attention from different angles, such as governmental issues, safety or hesitancy, and side effects. Moreover, Coronavirus-relevant phenomena like public vaccination and the rate of infection deeply impacted public emotional status and users' interactions.
Paper Structure (31 sections, 4 equations, 10 figures, 13 tables, 1 algorithm)

This paper contains 31 sections, 4 equations, 10 figures, 13 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Work
  3. Data
  4. Data Acquisition
  5. Data Preprocessing
  6. Removing Duplicates
  7. Text Cleaning
  8. Normalization
  9. Removing Stopwords
  10. Lemmatization
  11. Methods
  12. Topic Modeling
  13. Vaccine-related Tweets
  14. Vaccine-related Tweets Classification
  15. Bidirectional Encoder Representations from Transformers (BERT)
  16. ...and 16 more sections

Figures (10)

  • Figure 1: Data Acquisition and Preprocessing
  • Figure 2: Workflow of Twitter Analysis toward Covid-19 Vaccination
  • Figure 3: Relative Percentage of Vaccine Tweets Over Time
  • Figure 4: Happiness Trend of Vaccine-Related Tweets during Covid-19 Pandemic
  • Figure 5: Anger Trend of Vaccine-Related Tweets during Covid-19 Pandemic
  • ...and 5 more figures