6 papers
An Explainable Machine Learning Framework for Railway Predictive Maintenance using Data Streams from the Metro Operator of Portugal
Silvia GarcÃa-Méndez, Francisco de Arriba-Pérez, Fátima Leal +3
This work contributes to a real-time data-driven predictive maintenance solution for Intelligent Transportation Systems. The proposed method implements a processing pipeline compri…
Identification and explanation of disinformation in wiki data streams
Francisco de Arriba-Pérez, Silvia GarcÃa-Méndez, Fátima Leal +2
Social media platforms, increasingly used as news sources for varied data analytics, have transformed how information is generated and disseminated. However, the unverified nature…
Exposing and Explaining Fake News On-the-Fly
Francisco de Arriba-Pérez, Silvia GarcÃa-Méndez, Fátima Leal +2
Social media platforms enable the rapid dissemination and consumption of information. However, users instantly consume such content regardless of the reliability of the shared data…
Online detection and infographic explanation of spam reviews with data drift adaptation
Francisco de Arriba-Pérez, Silvia GarcÃa-Méndez, Fátima Leal +2
Spam reviews are a pervasive problem on online platforms due to its significant impact on reputation. However, research into spam detection in data streams is scarce. Another conce…
Simulation, Modelling and Classification of Wiki Contributors: Spotting The Good, The Bad, and The Ugly
Silvia GarcÃa Méndez, Fátima Leal, Benedita Malheiro +4
Data crowdsourcing is a data acquisition process where groups of voluntary contributors feed platforms with highly relevant data ranging from news, comments, and media to knowledge…
Interpretable classification of wiki-review streams
Silvia GarcÃa Méndez, Fátima Leal, Benedita Malheiro +1
Wiki articles are created and maintained by a crowd of editors, producing a continuous stream of reviews. Reviews can take the form of additions, reverts, or both. This crowdsourci…