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Explainable Classification of Wiki Streams

2024; Springer International Publishing; Linguagem: Inglês

10.1007/978-3-031-45642-8_7

2367-3370

Silvia García-Méndez, Fátima Leal, Francisco de Arriba-Pérez, Benedita Malheiro, Juan C. Burguillo,

Multimodal Machine Learning Applications

Web 2.0 platforms, like wikis and social networks, rely on crowdsourced data and, as such, are prone to data manipulation by ill-intended contributors. This research proposes the transparent identification of wiki manipulators through the classification of contributors as benevolent or malevolent humans or bots, together with the explanation of the attributed class labels. The system comprises: (i) stream-based data pre-processing; (ii) incremental profiling; and (iii) online classification, evaluation and explanation. Particularly, the system profiles contributors and contributions by combining features directly collected with content- and side-based engineered features. The experimental results obtained with a real data set collected from Wikivoyage – a popular travel wiki – attained a 98.52% classification accuracy and 91.34% macro F-measure. In the end, this work seeks to address data reliability to prevent information detrimental and manipulation.