TY - JOUR
AU - Amujo, Olasoji
AU - Ibeke, Ebuka
AU - Fuzi, Richard
AU - Ogara, Ugo
AU - Iwendi, Celestine
PY - 2023
DA - February
TI - Sentiment computation of UK-originated Covid-19 vaccine Tweets: a chronological analysis and news effect.
JO - Sustainability
AB - This study aimed to analyse public sentiments of UK-originated tweets about COVID-19 vaccines using six chronological data periods between January and December 2021. The dates are based on six BBC news reports about the most significant developments in the three main vaccines administered in the UK - Pfizer-BioNTech, Moderna, and Oxford-AstraZeneca. Each data period spans seven days, starting from the news report. The study employed the Bidirectional Encoder Representations from Transformers (BERT) model to analyse the sentiments in the 4,172 extracted tweets. The BERT model adopts the transformer architecture and uses the 'Masked Language Model' and 'Next Sentence Prediction'. The results show that the overall sentiments for all three vaccines were negative across all six periods, with Moderna having the least negative tweets and the highest percentage of positive tweets overall, while AstraZeneca attracted the most negative tweets. However, for all the considered periods, period 3 (23 -29 May 2021) received the least negative and the most positive tweets, following the BBC report – COVID - Pfizer and AstraZeneca jabs work against Indian variant, despite reports of blood clot cases associated with AstraZeneca in the same period. Periods 5 to 6, where there was no breaking news relating to COVID Vaccines, had no significant changes. We, therefore, conclude that the BBC News reports on COVID Vaccines significantly impacted public sentiments regarding the COVID-19 Vaccines.
PB - MDPI
UR - https://rgu-repository.worktribe.com/output/1879567
KW - Coronavirus (COVID-19)
ER