Twitter-Based Sentiment Analysis and Topic Modeling of Social Media Posts Using Natural Language Processing, to Understand People's Perspectives Regarding COVID-19 Booster Vaccine Shots in India: Crucial to Expanding Vaccination Coverage

Fight Against the Mandatory COVID-19 Immunity Passport on Twitter: Natural Language Processing Study

BACKGROUND

To contain and curb the spread of COVID-19, the governments of countries around the world have used different strategies (lockdown, mandatory vaccination, immunity passports, voluntary social distancing, etc).

OBJECTIVE

This study aims to examine the reactions produced by the public announcement of a binding political decision presented by the president of the French Republic, Emmanuel Macron, on July 12, 2021, which imposed vaccination on caregivers and an immunity passport on all French people to access restaurants, cinemas, bars, and so forth.

METHODS

To measure these announcement reactions, 901,908 unique tweets posted on Twitter (Twitter Inc) between July 12 and August 11, 2021, were extracted. A neural network was constructed to examine the arguments of the tweets and to identify the types of arguments used by Twitter users.

RESULTS

This study shows that in the debate about mandatory vaccination and immunity passports, mostly "con" arguments (399,803/847,725, 47%; χ26=952.8; P<.001) and "scientific" arguments (317,156/803,583, 39%; χ26=5006.8; P<.001) were used.

CONCLUSIONS

This study shows that during July and August 2021, social events permeating the public sphere and discussions about mandatory vaccination and immunity passports collided on Twitter. Moreover, a political decision based on scientific arguments led citizens to challenge it using pseudoscientific arguments contesting the effectiveness of vaccination and the validity of these political decisions.

Can ChatGPT be Trusted for Consulting? Uncovering Doctor's Perceptions Using Deep Learning Techniques

Since the introduction of ChatGPT by OpenAI in late 2022, the question of whether doctors can employ it for consultation has been a subject of debate. ChatGPT is a deep learning model trained on a vast dataset, but concerns about the reliability of its output have been a subject of debate in recent times. In this article, we have employed cutting-edge bidirectional encoder representations from transformers (BERT) sentiment analysis and topic modeling techniques to comprehend doctors' attitudes toward using ChatGPT in consultation.

COVID-19 vaccination perception and outcome: society sentiment analysis on twitter data in India

This study examines the perceptions and results of COVID-19 immunization using sentiment analysis of Twitter data from India. The tweets were collected from January 2021 to March 2023 using relevant hashtags and keywords. The dataset was pre-processed and cleaned before conducting sentiment analysis using Natural Language Processing techniques. Our results show that the overall sentiment toward COVID-19 vaccination in India has been positive, with a majority of tweets expressing support for vaccination and encouraging others to get vaccinated. However, we also identified some negative sentiments related to vaccine hesitancy, side effects, and mistrust in the government and pharmaceutical companies. We further analyzed the sentiment based on demographic factors such as gender, age, and location. The analysis revealed that the sentiment varied across different demographics, with some groups expressing more positive or negative sentiments than others. This study provides insights into the perception and outcomes of COVID-19 vaccination in India and highlights the need for targeted communication strategies to address vaccine hesitancy and increase vaccine uptake in specific demographics.

Modeling Behavior and Vaccine Hesitancy Using Twitter-Derived US Population Sentiment during the COVID-19 Pandemic to Predict Daily Vaccination Inoculations

The sentiment analysis of social media for predicting behavior during a pandemic is seminal in nature. As an applied contribution, we present sentiment-based regression models for predicting the United States COVID-19 first dose, second dose, and booster daily inoculations from 1 June 2021 to 31 March 2022. The models merge independent variables representing fear of the virus and vaccine hesitancy. Large correlations exceeding 77% and 84% for the first-dose and booster-dose models inspire confidence in the merger of the independent variables. Death count as a traditional measure of fear is a lagging indicator of inoculations, while Twitter-positive and -negative tweets are strong predictors of inoculations. Thus, the use of sentiment analysis for predicting inoculations is strongly supported with administrative events being catalysts for tweets. Non-inclusion in the second-dose regression model of data occurring before the 1 June 2021 timeframe appear to limit the second-dose model results-only achieving a moderate correlation exceeding 53%. Limiting tweet collection to geolocated tweets does not encompass the entire US Twitter population. Nonetheless, results from Kaiser Family Foundation (KFF) surveys appear to generally support the regression factors common to the first-dose and booster-dose regression models and their results.