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Detecting health misinformation: A comparative analysis of machine learning and graph convolutional networks in classification tasks.
Khemani, Bharti; Patil, Shruti; Kotecha, Ketan; Vora, Deepali.
Afiliación
  • Khemani B; Symbiosis Institute of Technology, Symbiosis International (Deemed University, Pune, India.
  • Patil S; Symbiosis Centre for Applied Artificial Intelligence (SCAAI), Symbiosis Institute of Technology Pune Campus, Symbiosis International (Deemed University) (SIU), Lavale, Pune 412115, India.
  • Kotecha K; Symbiosis Centre for Applied Artificial Intelligence (SCAAI), Symbiosis Institute of Technology Pune Campus, Symbiosis International (Deemed University) (SIU), Lavale, Pune 412115, India.
  • Vora D; Symbiosis Institute of Technology, Symbiosis International (Deemed University, Pune, India.
MethodsX ; 12: 102737, 2024 Jun.
Article en En | MEDLINE | ID: mdl-38774687
ABSTRACT
In the digital age, the proliferation of health-related information online has heightened the risk of misinformation, posing substantial threats to public well-being. This research conducts a meticulous comparative analysis of classification models, focusing on detecting health misinformation. The study evaluates the performance of traditional machine learning models and advanced graph convolutional networks (GCN) across critical algorithmic metrics. The results comprehensively understand each algorithm's effectiveness in identifying health misinformation and provide valuable insights for combating the pervasive spread of false health information in the digital landscape. GCN with TF-IDF gives the best result, as shown in the result section. •The research method involves a comparative analysis of classification algorithms to detect health misinformation, exploring traditional machine learning models and graph convolutional networks.•This research used algorithms such as Passive Aggressive Classifier, Random Forest, Decision Tree, Logistic Regression, Light GBM, GCN, GCN with BERT, GCN with TF-IDF, and GCN with Word2Vec were employed. Performance Metrics Accuracy for Passive Aggressive Classifier 85.75 %, Random Forest 86 %, Decision Tree 81.30 %, Light BGM 83.29 %, normal GCN 84.53 %, GCN with BERT 85.00 %, GCN with TR-IDF 93.86 % and GCN with word2Vec 81.00 %•Algorithmic performance metrics, including accuracy, precision, recall, and F1-score, were systematically evaluated to assess the efficacy of each model in detecting health misinformation, focusing on understanding the strengths and limitations of different approaches. The superior performance of Graph Convolutional Networks (GCNs) with TF-IDF embedding, achieving an accuracy of 93.86.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: MethodsX Año: 2024 Tipo del documento: Article País de afiliación: India Pais de publicación: Países Bajos
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: MethodsX Año: 2024 Tipo del documento: Article País de afiliación: India Pais de publicación: Países Bajos