<![CDATA[The swift dissemination of information regarding disease outbreaks via social media presents significant challenges in the automatic identification of pertinent outbreak-related messages, particularly within multilingual textual data. The objective of this research is to devise and assess classification models grounded in Graph Neural Networks for the detection of disease outbreak messages within Indonesian and English datasets. The methodology employed encompasses text normalisation, feature extraction utilizing TF-IDF and word embedding techniques, including Word2Vec, fastText, and GloVe, followed by classification employing Graph Convolutional Networks (GCN) and Graph Attention Networks (GAT). The findings of this study indicate that the optimal performance on the Indonesian dataset was attained by GCN utilizing TF-IDF features amounting to 5000, resulting in an accuracy of 93%, whereas the superior GAT model attained 92% accuracy utilizing 4000 TF-IDF features. For the English dataset, the most effective GCN model reached an accuracy of 86.77% employing fastText, while the leading GAT model achieved 86.79% with the same fastText feature set. These results surpass those of previous investigations that employed CNN, LSTM, and BERT-based models for analogous tasks of disease outbreak message classification. This study concludes that GCN and GAT represent efficacious deep learning methodologies for the classification of multilingual disease outbreak messages. GCN demonstrates superior performance on Indonesian text utilizing TF-IDF-based features, while GAT exhibits marginally better performance on English text employing fastText features. In summary, graph-based models are capable of capturing relational information among textual features and delivering competitive classification performance when juxtaposed with earlier deep learning methodologies.]]>
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