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Nos últimos anos, a disseminação de desinformação por meio das plataformas digitais tem comprometido a saúde pública, a estabilidade social e até mesmo os processos democráticos. No contexto brasileiro, o aplicativo de mensagens WhatsApp é reconhecidamente uma das principais fontes de disseminação de desinformação. Este trabalho propõe uma abordagem para a detecção automatizada de desinformadores no WhatsApp, tratando o problema como uma tarefa de classificação de usuários com base em suas interações na rede. Para isso, os usuários e suas conexões são modelados como um grafo. Em seguida, uma arquitetura de rede neural explora o grafo previamente construído para capturar padrões estruturais associados à propagação de desinformação. Neste trabalho, os modelos GCN, GCNII, GraphSAGE, GAT e APPNP são avaliados por meio da validação cruzada estratificada, utilizando as métricas de acurácia, precisão, revocação, F1-score e  AUC-ROC. Os resultados mostram que as GNNs capturam padrões topológicos relevantes para a identificação de desinformadores, o que indica seu potencial para o combate à desinformação em ambientes digitais.

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In recent years, the dissemination of misinformation through digital platforms has compromised public health, social stability, and even democratic processes. In the Brazilian context, WhatsApp is widely recognized as one of the main sources of misinformation. This study proposes an approach to automatically detect misinformation spreaders on WhatsApp by formulating the problem as a user classification task based on their interactions within the network. To this end, users and their connections are modeled as a graph. Subsequently, a neural network architecture leverages the previously constructed graph to capture structural patterns associated with the spread of misinformation. In this work, the GCN, GCNII, GraphSAGE, GAT, and APPNP models are evaluated stratified cross-validation using accuracy, precision, recall, F1-score, and AUC-ROC as performance metrics. The results show that GNNs can capture topological patterns relevant to identifying misinformation spreaders, indicating their potential to combat misinformation in digital environments.

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Authors

#	Name
1	Saulo Cavalcante(saulocavalcante@alu.ufc.br)
2	Mikael Sales(mikaelmfsales@gmail.com)
3	Dagoberto Silva(dagobertosilva@alu.ufc.br)
4	Fernanda Paulino(fernandaferreira@alu.ufc.br)
5	Wellington Franco(wellington@crateus.ufc.br)
6	Rennan Dantas(rennan@ufc.br)
7	Jose Monteiro(monteiro@dc.ufc.br)

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Reference

#	Reference
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