Monitoring User Opinions and Side Effects on COVID-19 Vaccines in the Twittersphere: Infodemiology Study of Tweets

Abstract

Background In the current phase of the COVID-19 pandemic, we are witnessing the most massive vaccine rollout in human history. Like any other drug, vaccines may cause unexpected side effects, which need to be investigated in a timely manner to minimize harm in the population. If not properly dealt with, side effects may also impact public trust in the vaccination campaigns carried out by national governments. Objective Monitoring social media for the early identification of side effects, and understanding the public opinion on the vaccines are of paramount importance to ensure a successful and harmless rollout. The objective of this study was to create a web portal to monitor the opinion of social media users on COVID-19 vaccines, which can offer a tool for journalists, scientists, and users alike to visualize how the general public is reacting to the vaccination campaign. Methods We developed a tool to analyze the public opinion on COVID-19 vaccines from Twitter, exploiting, among other techniques, a state-of-the-art system for the identification of adverse drug events on social media; natural language processing models for sentiment analysis; statistical tools; and open-source databases to visualize the trending hashtags, news articles, and their factuality. All modules of the system are displayed through an open web portal. Results A set of 650,000 tweets was collected and analyzed in an ongoing process that was initiated in December 2020. The results of the analysis are made public on a web portal (updated daily), together with the processing tools and data. The data provide insights on public opinion about the vaccines and its change over time. For example, users show a high tendency to only share news from reliable sources when discussing COVID-19 vaccines (98% of the shared URLs). The general sentiment of Twitter users toward the vaccines is negative/neutral; however, the system is able to record fluctuations in the attitude toward specific vaccines in correspondence with specific events (eg, news about new outbreaks). The data also show how news coverage had a high impact on the set of discussed topics. To further investigate this point, we performed a more in-depth analysis of the data regarding the AstraZeneca vaccine. We observed how media coverage of blood clot–related side effects suddenly shifted the topic of public discussions regarding both the AstraZeneca and other vaccines. This became particularly evident when visualizing the most frequently discussed symptoms for the vaccines and comparing them month by month. Conclusions We present a tool connected with a web portal to monitor and display some key aspects of the public’s reaction to COVID-19 vaccines. The system also provides an overview of the opinions of the Twittersphere through graphic representations, offering a tool for the extraction of suspected adverse events from tweets with a deep learning model.

Users’ Reactions to Announced Vaccines Against COVID-19 Before Marketing in France: Analysis of Twitter Posts

“…In previous work, several custom algorithms were proposed, such as (1) deep learning with a CamemBERT model [ 28 ], BERT [ 46 , 47 ], RoBERTa [ 48 ], FastText [ 49 ], convolutional neural network–long short-term memory with word2vec embeddings [ 50 ] or (2) machine learning with naïve Bayes [ 51 ] or decision tree [ 52 ] models. Off-the-shelf sentiment analysis models include Amazon Web Services Comprehend sentiment analysis [ 53 ] and VADER [ 54 - 60 ], which is a Python lexicon and rule-based sentiment analysis tool [ 43 ].…”

“…Kummervold et al [ 46 ] obtained an F 1 -score of 0.78 to predict the attitude of pregnant women toward vaccination against COVID-19, but the categories for the classification were different. Portelli et al [ 48 ] used a RoBERTa model trained on TweetEval Benchmark. However, the authors only provided the recall for sentiment analysis, which was 72.1, and not the precision, which does not allow a comparison with our results.…”

Applications of Social Media and Digital Technologies in COVID-19 Vaccination: Scoping Review

“…Additional machine learning techniques for polarity classification were Microsoft Azure cognitive services, Amazon Web Services (AWS), and Baidu’s AipNLP [ 59 , 64 , 75 ]. Deep learning techniques mainly used convolutional neural networks, recurrent neural networks, bidirectional long short-term memory (LSTM), and Bidirectional Encoder Representations from Transformers [ 63 , 65 , 66 , 68 , 71 , 77 ].…”

“…Although public sentiments on COVID-19 vaccines varied significantly over time and geography [ 7 , 33 , 39 ], positive sentiments were more prevalent than negative ones regarding COVID-19 vaccines [ 7 , 19 , 20 , 22 , 25 , 29 , 33 , 41 - 44 , 46 , 47 , 51 , 57 , 60 , 62 , 65 , 70 , 72 , 76 , 78 , 84 , 85 ], with trust and anticipation being the predominant emotions [ 20 , 23 , 32 , 37 , 50 , 54 , 58 ]. However, some other studies found that negative sentiments overwhelmed positive ones, with fear being the dominant emotion [ 53 , 59 , 64 , 66 , 71 , 73 , 79 - 82 , 86 ]. Positive sentiments were found to be mainly related to increased vaccine coverage, vaccine development, vaccination research, and health services [ 31 , 57 , 67 , 69 ], whereas negative sentiments were positively associated with increased COVID-19 cases, misinformation, conspiracy theories, and fear regarding vaccine safety [ 21 , 55 ].…”

“…Nineteen sentiment analysis studies were based on machine learning, and they trained machine learning classifiers by annotating tweets in the data set [59][60][61][62][63][64][65][66][67][68][69][70][71][72][73][74][75][76][77]. Machine learning approaches used supervised classification algorithms to extract information regarding sentiment polarity.…”