Analyzing Sentiments and Diffusion Characteristics of COVID-19 Vaccine Misinformation Topics in Social Media

Daradkeh, Mohammad

doi:10.4018/ijban.292056

Cited by 16 publications

(10 citation statements)

References 80 publications

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“…Fifty-three studies applied social media for topic analysis on COVID-19 vaccination. Topic analysis methods included latent Dirichlet allocation (LDA) topic modeling (n=24) [ 20 , 23 , 25 , 31 , 34 , 35 , 40 , 47 , 50 , 51 , 54 - 56 , 58 , 60 , 61 , 64 , 87 - 93 ], manual coding (n=17) [ 57 , 80 , 94 - 108 ], and other algorithms (n=12) [ 26 , 43 , 62 , 109 - 117 ]. Table 2 summarizes the provaccine and antivaccine topics on COVID-19 vaccines present on social media.…”

Section: Resultsmentioning

confidence: 99%

“…Regarding information sources, authoritative and reliable information disseminators, such as government agencies, major media outlets, and key opinion leaders, played massively influential roles in polarizing opinions, which can amplify or contain the spread of misinformation among target audiences. Positive discourses were more likely to interact with verified sources, such as news organizations, health professionals, and media/journalists, while negative discourses tended to interact with politicians and personal accounts [ 40 , 114 ].…”

Section: Resultsmentioning

confidence: 99%

“…Forty studies applied the lexicon-based sentiment analysis method, using predefined lexicons annotated with sentiment polarities (eg, positive, negative, or neutral) to determine sentiments expressed in the parsed text . TextBlob and VADER (Valence Aware Dictionary and Sentiment Reasoner) were 2 well-known rule-based lexical sentiment analyzers [19,25,26,33,[37][38][39][40][41]43,44,46,49,51,53,[56][57][58]. Moreover, 11 studies further predicted the emotion types expressed in the tweets [20,21,23,26,32,37,45,50,53,54,58], and 8 classified emotions as "trust, surprise, sadness, joy, anticipation, disgust, fear, and anger" using the National Research Council Sentiment Lexicon [20,23,32,37,45,50,54,58].…”

Section: Sentiment/emotion Analysismentioning

confidence: 99%

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

et al. 2023

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Background Social media and digital technologies have played essential roles in disseminating information and promoting vaccination during the COVID-19 pandemic. There is a need to summarize the applications and analytical techniques of social media and digital technologies in monitoring vaccine attitudes and administering COVID-19 vaccines. Objective We aimed to synthesize the global evidence on the applications of social media and digital technologies in COVID-19 vaccination and to explore their avenues to promote COVID-19 vaccination. Methods We searched 6 databases (PubMed, Scopus, Web of Science, Embase, EBSCO, and IEEE Xplore) for English-language articles from December 2019 to August 2022. The search terms covered keywords relating to social media, digital technology, and COVID-19 vaccines. Articles were included if they provided original descriptions of applications of social media or digital health technologies/solutions in COVID-19 vaccination. Conference abstracts, editorials, letters, commentaries, correspondence articles, study protocols, and reviews were excluded. A modified version of the Appraisal Tool for Cross-Sectional Studies (AXIS tool) was used to evaluate the quality of social media–related studies. The review was undertaken with the guidance of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews. Results A total of 178 articles were included in our review, including 114 social media articles and 64 digital technology articles. Social media has been applied for sentiment/emotion analysis, topic analysis, behavioral analysis, dissemination and engagement analysis, and information quality analysis around COVID-19 vaccination. Of these, sentiment analysis and topic analysis were the most common, with social media data being primarily analyzed by lexicon-based and machine learning techniques. The accuracy and reliability of information on social media can seriously affect public attitudes toward COVID-19 vaccines, and misinformation often leads to vaccine hesitancy. Digital technologies have been applied to determine the COVID-19 vaccination strategy, predict the vaccination process, optimize vaccine distribution and delivery, provide safe and transparent vaccination certificates, and perform postvaccination surveillance. The applied digital technologies included algorithms, blockchain, mobile health, the Internet of Things, and other technologies, although with some barriers to their popularization. Conclusions The applications of social media and digital technologies in addressing COVID-19 vaccination–related issues represent an irreversible trend. Attention should be paid to the ethical issues and health inequities arising from the digital divide while applying and promoting these technologies.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Sentiment/emotion Analysismentioning

confidence: 99%

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

et al. 2023

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“…In text mining applications, deep learning models can automatically find features from distributed term vectors, which are highly portable and efficient to learn compared to conventional machine learning algorithms such as support vector machines (SVM) and conditional random fields (CRF) [44,45]. However, the quality of initial features still affects the efficiency of deep learning, and low-quality features tend to be overfitted or underfitted [46].…”

Section: Feature Matrix Construction and Vectorizationmentioning

confidence: 99%

Scientometric Analysis and Classification of Research Using Convolutional Neural Networks: A Case Study in Data Science and Analytics

et al. 2022

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With the increasing development of published literature, classification methods based on bibliometric information and traditional machine learning approaches encounter performance challenges related to overly coarse classifications and low accuracy. This study presents a deep learning approach for scientometric analysis and classification of scientific literature based on convolutional neural networks (CNN). Three dimensions, namely publication features, author features, and content features, were divided into explicit and implicit features to form a set of scientometric terms through explicit feature extraction and implicit feature mapping. The weighted scientometric term vectors are fitted into a CNN model to achieve dual-label classification of literature based on research content and methods. The effectiveness of the proposed model is demonstrated using an application example from the data science and analytics literature. The empirical results show that the scientometric classification model proposed in this study performs better than comparable machine learning classification methods in terms of precision, recognition, and F1-score. It also exhibits higher accuracy than deep learning classification based solely on explicit and dominant features. This study provides a methodological guide for fine-grained classification of scientific literature and a thorough investigation of its practice.

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“…The dataset included tweets from different states of the USA for 15 days. A total number of 832,528 tweets were gathered The following results of classification were achieved with ML algorithms: an LR (0.91), an SVM (0.94), an NB (0.91), k-NN (0.90), a DT (0.96), a RF (0.97), and XGBoost (0.83) Daradkeh et al [ 72 ] This paper describes SA of topics related to Covid-19 vaccine misinformation. A corpus of 40,359 tweets has been collected for the dates between January 2021 and March 2021 It got the following values of accuracy: a DT (0.81), an SVM (0.78), a k-NN (0.76), and an NB (0.74) Mishra et al [ 73 ] This research paper analyzed the public’s sentiments towards the Covid-19 vaccination in India.…”

Section: Experimental Partmentioning

confidence: 99%

On the development of an information system for monitoring user opinion and its role for the public

et al. 2022

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Social media services and analytics platforms are rapidly growing. A large number of various events happen mostly every day, and the role of social media monitoring tools is also increasing. Social networks are widely used for managing and promoting brands and different services. Thus, most popular social analytics platforms aim for business purposes while monitoring various social, economic, and political problems remains underrepresented and not covered by thorough research. Moreover, most of them focus on resource-rich languages such as the English language, whereas texts and comments in other low-resource languages, such as the Russian and Kazakh languages in social media, are not represented well enough. So, this work is devoted to developing and applying the information system called the OMSystem for analyzing users’ opinions on news portals, blogs, and social networks in Kazakhstan. The system uses sentiment dictionaries of the Russian and Kazakh languages and machine learning algorithms to determine the sentiment of social media texts. The whole structure and functionalities of the system are also presented. The experimental part is devoted to building machine learning models for sentiment analysis on the Russian and Kazakh datasets. Then the performance of the models is evaluated with accuracy, precision, recall, and F1-score metrics. The models with the highest scores are selected for implementation in the OMSystem. Then the OMSystem’s social analytics module is used to thoroughly analyze the healthcare, political and social aspects of the most relevant topics connected with the vaccination against the coronavirus disease. The analysis allowed us to discover the public social mood in the cities of Almaty and Nur-Sultan and other large regional cities of Kazakhstan. The system’s study included two extensive periods: 10-01-2021 to 30-05-2021 and 01-07-2021 to 12-08-2021. In the obtained results, people’s moods and attitudes to the Government’s policies and actions were studied by such social network indicators as the level of topic discussion activity in society, the level of interest in the topic in society, and the mood level of society. These indicators calculated by the OMSystem allowed careful identification of alarming factors of the public (negative attitude to the government regulations, vaccination policies, trust in vaccination, etc.) and assessment of the social mood.

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Analyzing Sentiments and Diffusion Characteristics of COVID-19 Vaccine Misinformation Topics in Social Media

Cited by 16 publications

References 80 publications

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

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

Scientometric Analysis and Classification of Research Using Convolutional Neural Networks: A Case Study in Data Science and Analytics

On the development of an information system for monitoring user opinion and its role for the public

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