Uncovering the Reasons Behind COVID-19 Vaccine Hesitancy in Serbia: Sentiment-Based Topic Modeling

Abstract

Background Since the first COVID-19 vaccine appeared, there has been a growing tendency to automatically determine public attitudes toward it. In particular, it was important to find the reasons for vaccine hesitancy, since it was directly correlated with pandemic protraction. Natural language processing (NLP) and public health researchers have turned to social media (eg, Twitter, Reddit, and Facebook) for user-created content from which they can gauge public opinion on vaccination. To automatically process such content, they use a number of NLP techniques, most notably topic modeling. Topic modeling enables the automatic uncovering and grouping of hidden topics in the text. When applied to content that expresses a negative sentiment toward vaccination, it can give direct insight into the reasons for vaccine hesitancy. Objective This study applies NLP methods to classify vaccination-related tweets by sentiment polarity and uncover the reasons for vaccine hesitancy among the negative tweets in the Serbian language. Methods To study the attitudes and beliefs behind vaccine hesitancy, we collected 2 batches of tweets that mention some aspects of COVID-19 vaccination. The first batch of 8817 tweets was manually annotated as either relevant or irrelevant regarding the COVID-19 vaccination sentiment, and then the relevant tweets were annotated as positive, negative, or neutral. We used the annotated tweets to train a sequential bidirectional encoder representations from transformers (BERT)-based classifier for 2 tweet classification tasks to augment this initial data set. The first classifier distinguished between relevant and irrelevant tweets. The second classifier used the relevant tweets and classified them as negative, positive, or neutral. This sequential classifier was used to annotate the second batch of tweets. The combined data sets resulted in 3286 tweets with a negative sentiment: 1770 (53.9%) from the manually annotated data set and 1516 (46.1%) as a result of automatic classification. Topic modeling methods (latent Dirichlet allocation [LDA] and nonnegative matrix factorization [NMF]) were applied using the 3286 preprocessed tweets to detect the reasons for vaccine hesitancy. Results The relevance classifier achieved an F-score of 0.91 and 0.96 for relevant and irrelevant tweets, respectively. The sentiment polarity classifier achieved an F-score of 0.87, 0.85, and 0.85 for negative, neutral, and positive sentiments, respectively. By summarizing the topics obtained in both models, we extracted 5 main groups of reasons for vaccine hesitancy: concern over vaccine side effects, concern over vaccine effectiveness, concern over insufficiently tested vaccines, mistrust of authorities, and conspiracy theories. Conclusions This paper presents a combination of NLP methods applied to find the reasons for vaccine hesitancy in Serbia. Given these reasons, it is now possible to better understand the concerns of people regarding the vaccination process.

A natural language processing approach for analyzing COVID-19 vaccination response in multi-language and geo-localized tweets

“…Ljajic et al. [23] identify the reasons for vaccine hesitancy in negative tweets written in Serbian, employing NLP techniques and classifying tweets related to vaccination based on their sentiment polarity.…”

Myotonic dystrophy type 1 in the COVID-19 era

“…A recent survey conducted in Serbia in general population lists five reasons for the population's hesitancy about getting vaccinated. They refer to the side effects of the vaccine, concerns over their effectiveness, concern over insufficiently tested vaccines, distrust of authorities, and conspiracy theories [ 24 ]. Some of these factors may also be related to DM1.…”

Year 2022 in Medical Natural Language Processing: Availability of Language Models as a Step in the Democratization of NLP in the Biomedical Area

“…• Malayalam: generation of synoptic clinical reports [39]; • Polish: prediction of cardiovascular diseases in electronic health records [40]; • (Brazilian) Portuguese: description of an annotated clinical corpus [41], ICD-10 coding [42]; • Serbian: sentiment analysis in COVID-19 tweets [43]; • Spanish: ICD-coding [10, 44], negation and uncertainty detection in clinical narratives [45], training and evaluation of word embeddings for the clinical domain [46]; • Swedish: ICD-10 coding [44];…”

“…and institutions (like MIMIC-III), as well as data from social media, hospitals, bibliographical datasets, clinical trials, etc. The research in other languages is possible mainly thanks to the availability of data from social media [7,9,19,20,22,38,43,47] and documents from local hospitals [10,13,14,17,18,23,25,27,36,37,40,42]. Besides, this set of works in languages other than English relies on the dedicated language models, which cover a great variety of languages by now.…”