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Publication
Identification and explanation of disinformation in wiki data streams
| Name: | Description: | Size: | Format: | |
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| 2.22 MB | Adobe PDF |
Advisor(s)
Abstract(s)
Social media platforms, increasingly used as news sources for varied data analytics, have transformed how information is generated and disseminated. However, the unverified nature of this content raises concerns about trustworthiness and accuracy, potentially negatively impacting readers’ critical judgment due to disinformation. This work aims to contribute to the automatic data quality validation field, addressing the rapid growth of online content on wiki pages. Our scalable solution includes stream-based data processing with feature engineering, feature analysis and selection, stream-based classification, and real-time explanation of prediction outcomes. The explainability dashboard is designed for the general public, who may need more specialized knowledge to interpret the model’s prediction. Experimental results on two datasets attain approximately 90% values across all evaluation metrics, demonstrating robust and competitive performance compared to works in the literature. In summary, the system assists editors by reducing their effort and time in detecting disinformation.
Description
Keywords
Disinformation explainable artificial intelligence machine learning natural language processing stream processing wikis
Citation
Arriba-Pérez F de, García-Méndez S, Leal F, Malheiro B, Burguillo JC. Identification and explanation of disinformation in wiki data streams. Integrated Computer-Aided Engineering. 2025;0(0). doi:10.1177/10692509241306580
Publisher
SAGE Publications