On Public Sentiments Towards COVID–19 Vaccines in South African Cities: An Analysis of Twitter Posts. (Preprint)

Ogbuokiri, Blessing; Ahmadi, Ali; Bragazzi, Nicola Luigi; Nia, Zahra Movahedi; Mellado, B.; Wu, Jianhong; Orbinski, James; Asgary, Ali; Kong, Jude Dzevela

doi:10.2196/preprints.36796

“…Furthermore, the compound polarity was used to assign the sentiment such as, positive, negative, or neutral to a tweet as label. A tweet with a compound polarity ≥ 0.5 is assigned the label positive, <0 is assigned the label negative, and x, where x satisfies the inequality 0.5 > x ≥ 0 is assigned the label neutral [42].…”

Section: E Tweet Labelingmentioning

confidence: 99%

“…To validate the output of the tweet labeling done by the VADER pretrained model [44], we used five machine learning classifiers including NB [45], LR [46], [47], SVMs [47], [48], DT [49], and KNN [50]. The reason we chose these classifiers is because they have been successfully used to classify tweets in [42].…”

Section: F Tweet Sentiment Classificationmentioning

confidence: 99%

Vaccine Hesitancy Hotspots in Africa: An Insight From Geotagged Twitter Posts

Ogbuokiri

¹

,

Ahmadi

²

,

Nia

³

et al. 2024

IEEE Trans. Comput. Soc. Syst.

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Many social media users express concerns about vaccines and their side effects on Twitter. These concerns lead to a compromise of confidence which brings about vaccine hesitancy. In Africa, vaccine hesitancy is a major challenge faced by health policymakers in the fight against COVID-19. Given that most tweets are geotagged, clustering them according to their sentiments could help identify locations that may likely experience vaccine hesitancy for health policy and planning. In this study, we collected 70 000 geotagged vaccine-related tweets in nine African countries, from December 2020 to February 2022. The tweets were classified into three sentiment classespositive, negative, and neutral. The quality of the classification outputs was achieved using Naíve Bayes (NB), logistic regression (LR), support vector machines (SVMs), decision tree (DT), and K-nearest neighbor (KNN) machine learning classifiers. The LR achieved the highest accuracy of 71% with an average area under the curve of 85%. The point-based location technique was used to calculate the hotspots based on the locations of the classified tweets. Locations with green, red, and gray backgrounds on the map signify a hotspot for positive, negative, and neutral sentiments. The outcome of this research shows that discussions on social media can be analyzed to identify hotspots during a disease outbreak, which could inform health policy in planning and management of vaccine hesitancy in Africa.

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“…Further, the compound polarity was used to assigned the sentiment such as, positive, negative, or neutral to a tweet as label. A tweet with a compound polarity ≥ 0.5 is assigned the label positive, < 0 is assigned the label negative, and x, where x satisfies the inequality 0.5 > x ≥ 0 is assigned the label neutral [30].…”

Section: Tweet Labellingmentioning

confidence: 99%

“…To validate the output of the tweet labelling done by the VADER pre-trained model [31], we used five machine learning classifiers including Naive Bayes (NB) [32], Logistic Regression (LR) [33], [34], Support Vector Machines (SVMs) [34], [35], Decision Tree (DT) [36], and K-Nearest Neighbours (KNN) [37]. The reason we chose these classifiers is because they have been successfully used to classify tweets in [30] and [38].…”

Section: E Tweet Sentiment Classificationmentioning

confidence: 99%

Vaccine Hesitancy Hotspots in Africa: An Insight from Geotagged Twitter Posts

Ogbuokiri¹,

Ahmadi²,

Nia³

et al. 2022

Preprint

Self Cite

1

0

View full text Add to dashboard Cite

In this research, we collected 70,000 COVID–19 vaccine-related geotagged Twitter posts from nine African countries. The duration is from December 2020 to February 2022. We used VADER to classify the tweets into three sentiment classes (positive, negative, and neutral). The outputs were validated using machine learning classification algorithms, including, Naive Bayes, Logistic Regression, Support Vector Machines, Decision Tree, and K-Nearest Neighbour. We identified hotspots by clustering the sentiment of these tweets using the point-based location technique. These hotspots were visualised on the map using ArcGIS Online. On the map, we used green to represent positive sentiment dominance, red to represent negative sentiment dominance, and grey to represent neutral sentiment dominance. The outcome of this research shows that social media data can be used to complement existing data in identifying hotspots during future outbreaks, especially in the areas where there is little or no available data. It can also be used to inform health policy in managing vaccine hesitancy.

show abstract

Vaccine Hesitancy Hotspots in Africa: An Insight from Geotagged Twitter Posts

Ogbuokiri¹,

Ahmadi²,

Nia³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

In this research, we collected 70,000 COVID–19 vaccine-related geotagged Twitter posts from nine African countries. The duration is from December 2020 to February 2022. We used VADER to classify the tweets into three sentiment classes (positive, negative, and neutral). The outputs were validated using machine learning classification algorithms, including, Naive Bayes, Logistic Regression, Support Vector Machines, Decision Tree, and K-Nearest Neighbour. We identified hotspots by clustering the sentiment of these tweets using the point-based location technique. These hotspots were visualised on the map using ArcGIS Online. On the map, we used green to represent positive sentiment dominance, red to represent negative sentiment dominance, and grey to represent neutral sentiment dominance. The outcome of this research shows that social media data can be used to complement existing data in identifying hotspots during future outbreaks, especially in the areas where there is little or no available data. It can also be used to inform health policy in managing vaccine hesitancy.

show abstract

On Public Sentiments Towards COVID–19 Vaccines in South African Cities: An Analysis of Twitter Posts. (Preprint)

Cited by 3 publications

References 26 publications

Vaccine Hesitancy Hotspots in Africa: An Insight From Geotagged Twitter Posts

Vaccine Hesitancy Hotspots in Africa: An Insight From Geotagged Twitter Posts

Vaccine Hesitancy Hotspots in Africa: An Insight from Geotagged Twitter Posts

Vaccine Hesitancy Hotspots in Africa: An Insight from Geotagged Twitter Posts

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