Analyzing Public Perceptions Toward COVID-19 Vaccination Process Using Social Media and Machine Learning

Elhishi, Sara; El-Deeb, Reem; El-Gamal, Fatma El-Zahraa A.; Sakr, Nehal A.; El-Metwally, Sara

doi:10.1145/3485557.3485558

Cited by 2 publications

(3 citation statements)

References 11 publications

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“…The authors of [42] did not specify any location; however, their findings highlighted an urgent need for proactive engagement with people from different educational backgrounds and cultures to validate the vaccine-related news and promote vaccine awareness since their study found that the second biggest group had a negative opinion regarding the COVID-19 vaccine. Other studies, such as [3,41,71,72,85] and [86], revealed that the public is more concerned with the safety and effectiveness of the COVID-19 vaccine, given the conspiracy theories circling on several social media platforms.…”

Section: Findings For Rqmentioning

confidence: 95%

“…Challenges related to the nature of data mainly represent those aspects of data that hinder understanding the data. For instance, aspects such as the ambiguity of data and natural languages such as the use of irony, sarcasm, and slang language pose a challenge to data analysis [17,42,45,134]. Moreover, data noise in terms of grammatical errors and the use of incoherent text hinders a clear understanding of data [57,59].…”

Section: Nature Of Datamentioning

confidence: 99%

“…Other strategies include countering fake news [48,71] and dealing with personal vaccine hesitancy aspects [50] and the inability to know the validity of the information. There were also issues in monitoring and difficulty in understanding how monitoring strategies affected vaccine perceptions [42]. Other challenges with monitoring included population bias, varied monitoring influencers [14], and the inability to focus on a specific period or across different times.…”

Section: Strategiesmentioning

confidence: 99%

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Role of Artificial Intelligence for Analysis of COVID-19 Vaccination-Related Tweets: Opportunities, Challenges, and Future Trends

et al. 2022

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Pandemics and infectious diseases are overcome by vaccination, which serves as a preventative measure. Nevertheless, vaccines also raise public concerns; public apprehension and doubts challenge the acceptance of new vaccines. COVID-19 vaccines received a similarly hostile reaction from the public. In addition, misinformation from social media, contradictory comments from medical experts, and reports of worse reactions led to negative COVID-19 vaccine perceptions. Many researchers analyzed people’s varying sentiments regarding the COVID-19 vaccine using artificial intelligence (AI) approaches. This study is the first attempt to review the role of AI approaches in COVID-19 vaccination-related sentiment analysis. For this purpose, insights from publications are gathered that analyze the (a) approaches used to develop sentiment analysis tools, (b) major sources of data, (c) available data sources, and (d) the public perception of COVID-19 vaccine. Analysis suggests that public perception-related COVID-19 tweets are predominantly analyzed using TextBlob. Moreover, to a large extent, researchers have employed the Latent Dirichlet Allocation model for topic modeling of Twitter data. Another pertinent discovery made in our study is the variation in people’s sentiments regarding the COVID-19 vaccine across different regions. We anticipate that our systematic review will serve as an all-in-one source for the research community in determining the right technique and data source for their requirements. Our findings also provide insight into the research community to assist them in their future work in the current domain.

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Section: Findings For Rqmentioning

confidence: 95%

Section: Nature Of Datamentioning

confidence: 99%

Section: Strategiesmentioning

confidence: 99%

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Role of Artificial Intelligence for Analysis of COVID-19 Vaccination-Related Tweets: Opportunities, Challenges, and Future Trends

et al. 2022

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Automatically detecting and understanding the perception of COVID-19 vaccination: a middle east case study

Aljedaani

Abuhaimed

Rustam

et al. 2022

Soc. Netw. Anal. Min.

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Introduction The development of COVID-19 vaccines has been a great relief in many countries that have been affected by the pandemic. As a result, many governments have made significant efforts to purchase and administer vaccines to their populations. However, accommodating such vaccines is typically confronted with people’s reluctance and fear. Like any other important event, COVID-19 vaccines have attracted people’s discussions on social media and impacted their opinions about vaccination. Objective The goal of this study is twofold: First, it conducts a sentiment analysis around COVID-19 vaccines by automatically analyzing Arabic users’ tweets. This analysis has been spread over time to better capture the changes in vaccine perceptions. This will provide us with some insights into the most popular and accepted vaccine(s) in the Arab countries, as well as the reasons behind people’s reluctance to take the vaccine. Second, it develops models to detect any vaccine-related tweets, to help with gathering all information related to people’s perception of the virus, and potentially detecting vaccine-related tweets that are not necessarily tagged with the virus’s main hashtags. Methods Arabic Tweets were collected by the authors, starting from January 1st, 2021, until April 20th, 2021. We deployed various Natural Language Processing (NLP) to distill our selected tweets. The curated dataset included in the analysis consisted of 1,098,376 unique tweets. To achieve the first goal, we designed state-of-the-art sentiment analysis techniques to extract knowledge related to the degree of acceptance of all existing vaccines and what are the main obstacles preventing the wide audience from accepting them. To achieve the second goal, we tackle the detection of vaccine-related tweets as a binary classification problem, where various Machine Learning (ML) models were designed to identify such tweets regardless of whether they use the vaccine hashtags or not. Results Generally, we found that the highest positive sentiments were registered for Pfizer-BioNTech, followed by Sinopharm-BIBP and Oxford-AstraZeneca. In addition, we found that 38% of the overall tweets showed negative sentiment, and only 12% had a positive sentiment. It is important to note that the majority of the sentiments vary between neutral and negative, showing the lack of conviction of the importance of vaccination among the large majority of tweeters. This paper extracts the top concerns raised by the tweets and advocates for taking them into account when advertising for the vaccination. Regarding the identification of vaccine-related tweets, the Logistic Regression model scored the highest accuracy of 0.82. Our findings are concluded with implications for public health authorities and the scholarly community to take into account to improve the vaccine’s acceptance.

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Analyzing Public Perceptions Toward COVID-19 Vaccination Process Using Social Media and Machine Learning

Cited by 2 publications

References 11 publications

Role of Artificial Intelligence for Analysis of COVID-19 Vaccination-Related Tweets: Opportunities, Challenges, and Future Trends

Role of Artificial Intelligence for Analysis of COVID-19 Vaccination-Related Tweets: Opportunities, Challenges, and Future Trends

Automatically detecting and understanding the perception of COVID-19 vaccination: a middle east case study

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