A Large-Scale Analysis of Persian Tweets Regarding Covid-19 Vaccination
- URL: http://arxiv.org/abs/2302.04511v3
- Date: Sat, 13 Jan 2024 04:44:21 GMT
- Title: A Large-Scale Analysis of Persian Tweets Regarding Covid-19 Vaccination
- Authors: Taha ShabaniMirzaei, Houmaan Chamani, Amirhossein Abaskohi, Zhivar
Sourati Hassan Zadeh, Behnam Bahrak
- Abstract summary: 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.
We offer a comprehensive analysis of public opinion in Iran about the Coronavirus vaccines using data gathered from Twitter.
- Score: 1.2499537119440245
- License: http://creativecommons.org/licenses/by/4.0/
- 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.
Related papers
- An analysis of vaccine-related sentiments from development to deployment
of COVID-19 vaccines [0.31317409221921144]
We analyse Twitter sentiments from the beginning of the COVID-19 pandemic using a sentiment analysis framework via deep learning models.
Our results show a link between the number of tweets, the number of cases, and the change in sentiment polarity scores during major waves of COVID-19 cases.
arXiv Detail & Related papers (2023-06-23T22:10:05Z) - Dense Feature Memory Augmented Transformers for COVID-19 Vaccination
Search Classification [60.49594822215981]
This paper presents a classification model for detecting COVID-19 vaccination related search queries.
We propose a novel approach of considering dense features as memory tokens that the model can attend to.
We show that this new modeling approach enables a significant improvement to the Vaccine Search Insights (VSI) task.
arXiv Detail & Related papers (2022-12-16T13:57:41Z) - Doctors vs. Nurses: Understanding the Great Divide in Vaccine Hesitancy
among Healthcare Workers [64.1526243118151]
We find that doctors are overall more positive toward the COVID-19 vaccines.
Doctors are more concerned with the effectiveness of the vaccines over newer variants.
Nurses pay more attention to the potential side effects on children.
arXiv Detail & Related papers (2022-09-11T14:22:16Z) - Vaccine Discourse on Twitter During the COVID-19 Pandemic [0.7161783472741748]
This study investigates posts related to COVID-19 vaccines on Twitter and focuses on those which have a negative stance toward vaccines.
A dataset of 16,713,238 English tweets related to COVID-19 vaccines was collected.
We show that the negativity with respect to COVID-19 vaccines has decreased over time along with the vaccine roll-outs.
arXiv Detail & Related papers (2022-07-23T13:50:51Z) - "COVID-19 was a FIFA conspiracy #curropt": An Investigation into the
Viral Spread of COVID-19 Misinformation [60.268682953952506]
We estimate the extent to which misinformation has influenced the course of the COVID-19 pandemic using natural language processing models.
We provide a strategy to combat social media posts that are likely to cause widespread harm.
arXiv Detail & Related papers (2022-06-12T19:41:01Z) - Winds of Change: Impact of COVID-19 on Vaccine-related Opinions of
Twitter users [19.08902619892565]
Administering COVID-19 vaccines at a societal scale has been deemed as the most appropriate way to defend against the COVID-19 pandemic.
This global vaccination drive naturally fueled a possibility of Pro-Vaxxers and Anti-Vaxxers strongly expressing their supports and concerns regarding the vaccines on social media platforms.
The goal of this work is to improve this understanding using the lens of Twitter-discourse data.
arXiv Detail & Related papers (2021-11-20T19:33:51Z) - Sentiment Analysis and Topic Modeling for COVID-19 Vaccine Discussions [10.194753795363667]
We conduct an in-depth analysis of tweets related to the coronavirus vaccine on Twitter.
Results show that a majority of people are confident in the effectiveness of vaccines and are willing to get vaccinated.
Negative tweets are often associated with the complaints of vaccine shortages, side effects after injections and possible death after being vaccinated.
arXiv Detail & Related papers (2021-10-08T23:30:17Z) - American Twitter Users Revealed Social Determinants-related Oral Health
Disparities amid the COVID-19 Pandemic [72.44305630014534]
We collected oral health-related tweets during the COVID-19 pandemic from 9,104 Twitter users across 26 states.
Women and younger adults (19-29) are more likely to talk about oral health problems.
People from counties at a higher risk of COVID-19 talk more about tooth decay/gum bleeding and chipped tooth/tooth break.
arXiv Detail & Related papers (2021-09-16T01:10:06Z) - Insight from NLP Analysis: COVID-19 Vaccines Sentiments on Social Media [6.965634726663563]
We collected tweet posts by the UK and US residents from the Twitter API during the pandemic.
We performed sentiment analysis by VADER and proposed a new method that can count the individual's influence.
The results indicated that celebrities could lead the opinion shift on social media in vaccination progress.
arXiv Detail & Related papers (2021-06-08T03:37:22Z) - The illicit trade of COVID-19 vaccines on the dark web [55.45786602961871]
Early analyses revealed that dark web marketplaces (DWMs) started offering COVID-19 related products (e.g., masks and COVID-19 tests) as soon as the COVID-19 pandemic started.
Here, we broaden the scope and depth of previous investigations by analysing 194 DWMs until July 2021, including the crucial period in which vaccines became available.
We show that recreational drugs are the most affected among traditional DWMs product, with COVID-19 mentions steadily increasing since March 2020.
arXiv Detail & Related papers (2021-02-10T14:52:54Z) - Falling into the Echo Chamber: the Italian Vaccination Debate on Twitter [65.7192861893042]
We examine the extent to which the vaccination debate on Twitter is conductive to potential outreach to the vaccination hesitant.
We discover that the vaccination skeptics, as well as the advocates, reside in their own distinct "echo chambers"
At the center of these echo chambers we find the ardent supporters, for which we build highly accurate network- and content-based classifiers.
arXiv Detail & Related papers (2020-03-26T13:55:50Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.