Can Post-vaccination Sentiment Affect the Acceptance of Booster Jab?

Ogbuokiri, Blessing; Ahmadi, Ali; Garcia, B. R. Mellado; Wu, Jiahong; Orbinski, James; Asgary, Ali; Kong, Jude Dzevela

doi:10.1007/978-3-031-35501-1_20

Cited by 4 publications

(2 citation statements)

References 18 publications

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“…Social outcomes of emergent crises such as M-pox and COVID-19 pandemic, e.g. stigmatization, vaccine hesitancy, resistance against PIs and NPIs, could be alleviated only with social impositions and dissemination of true information [ 15 , 16 , 19 ]. Therefore, researchers have focused on developing NLP models that could extract meaningful information from mass opinions and social media conversation to inform decision-making.…”

Section: Discussionmentioning

confidence: 99%

“…Khan, et al [ 17 ] used Twitter to identify individual and community factors that cause vaccine hesitancy. Ogbuokiri B, et al [ 18 ] used Twitter to find vaccine hesitancy hotspots in South Africa on city-level, and in [ 19 ] they tried to understand the post-vaccination sentiment in Africa. This work verifies the superiority of hand-labeled data to automated labeled data by comparing lexicon-based automated labels with different transformer-based models; therefore, it could help developers build more efficient and dependable NLP models for disease control and emergency management in future outbreaks and epidemics [ 11 – 13 , 20 ].…”

Section: Related Workmentioning

confidence: 99%

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Evaluating automatic annotation of lexicon-based models for stance detection of M-pox tweets from May 1st to Sep 5th, 2022

Perikli,

Bhattacharya,

Ogbuokiri

et al. 2024

PLOS Digit Health

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Manually labeling data for supervised learning is time and energy consuming; therefore, lexicon-based models such as VADER and TextBlob are used to automatically label data. However, it is argued that automated labels do not have the accuracy required for training an efficient model. Although automated labeling is frequently used for stance detection, automated stance labels have not been properly evaluated, in the previous works. In this work, to assess the accuracy of VADER and TextBlob automated labels for stance analysis, we first manually label a Twitter, now X, dataset related to M-pox stance detection. We then fine-tune different transformer-based models on the hand-labeled M-pox dataset, and compare their accuracy before and after fine-tuning, with the accuracy of automated labeled data. Our results indicated that the fine-tuned models surpassed the accuracy of VADER and TextBlob automated labels by up to 38% and 72.5%, respectively. Topic modeling further shows that fine-tuning diminished the scope of misclassified tweets to specific sub-topics. We conclude that fine-tuning transformer models on hand-labeled data for stance detection, elevates the accuracy to a superior level that is significantly higher than automated stance detection labels. This study verifies that automated stance detection labels are not reliable for sensitive use-cases such as health-related purposes. Manually labeled data is more convenient for developing Natural Language Processing (NLP) models that study and analyze mass opinions and conversations on social media platforms, during crises such as pandemics and epidemics.

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Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Evaluating automatic annotation of lexicon-based models for stance detection of M-pox tweets from May 1st to Sep 5th, 2022

Perikli,

Bhattacharya,

Ogbuokiri

et al. 2024

PLOS Digit Health

Self Cite

View full text Add to dashboard Cite

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An Improved Ensemble Method for Predicting Hyperchloremia in Adults With Diabetic Ketoacidosis

Obaido,

Ogbuokiri,

Chukwu

et al. 2024

IEEE Access

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Diabetic ketoacidosis (DKA) is a serious complication that affects millions of individuals globally and presents significant health complications. Hyperchloremia, an electrolyte imbalance characterized by high levels of chloride in the blood, may result in gastrointestinal problems, kidney damage, and even death, especially in DKA patients. Early detection and treatment of hyperchloremia are of utmost importance in the management of DKA. This study explores the potential of the bootstrap aggregating ensemble with random subspaces machine learning approach to predict the occurrence of hyperchloremia, providing a basis for early intervention and improved patient outcomes. We tested our approach with the retrospective MIMIC-III database containing 1177 DKA patients and compared it with previous studies with an area under the curve (AUC) of 100%. Our approach showed significant performance outperforming other methods. The combination of this approach may enhance the early detection and timely intervention for hyperchloremia cases, ultimately leading to improved patient outcomes and a more effective management of DKA-associated complications. Our work aims to contribute to the development of decision support tools for healthcare professionals, assisting them in making informed decisions for DKA patients, with a focus on preventing and managing hyperchloremia. INDEX TERMS Boosting Aggregating or Bagging Classifier, Diabetic Ketoacidosis (DKA), Hyperchloremia, Machine Learning, Predictive Modelling I. INTRODUCTIONDiabetes, a metabolic disorder, disrupts the regulation of blood glucose levels which can lead to both short-term and long-term health complications, and in severe cases, even death if not effectively managed [1]-[5]. This condition is broadly categorized into two types: type 1 diabetes (TY1D) and type 2 diabetes (TY2D). The pancreas plays a vital role in producing insulin, and serves as a key regulator for blood sugar levels. Insulin acts as a major energy source for muscles and various tissues, facilitating the entry of blood sugar into the body's cells [6]. This process is particularly significant in understanding both TY1D and TY2D as disruptions in insulin production or its effectiveness can lead to distinct metabolic imbalances and complications associated with each type. Both TY1D and TY2D diabetes are chronic diseases that affect millions of people worldwide

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An Improved Framework for Detecting Thyroid Disease Using Filter-Based Feature Selection and Stacking Ensemble

Obaido,

Achilonu,

Ogbuokiri

et al. 2024

IEEE Access

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Can Post-vaccination Sentiment Affect the Acceptance of Booster Jab?

Cited by 4 publications

References 18 publications

Evaluating automatic annotation of lexicon-based models for stance detection of M-pox tweets from May 1st to Sep 5th, 2022

Evaluating automatic annotation of lexicon-based models for stance detection of M-pox tweets from May 1st to Sep 5th, 2022

An Improved Ensemble Method for Predicting Hyperchloremia in Adults With Diabetic Ketoacidosis

An Improved Framework for Detecting Thyroid Disease Using Filter-Based Feature Selection and Stacking Ensemble

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