  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. Consequently, the latter crowdsourcing model is
exposed to manipulation. This work contributes with an explainable and online
classification method to recognize fake news in real-time. The proposed method
combines both unsupervised and supervised Machine Learning approaches with
online created lexica. The profiling is built using creator-, content- and
context-based features using Natural Language Processing techniques. The
explainable classification mechanism displays in a dashboard the features
selected for classification and the prediction confidence. The performance of
the proposed solution has been validated with real data sets from Twitter and
the results attain 80 % accuracy and macro F-measure. This proposal is the
first to jointly provide data stream processing, profiling, classification and
explainability. Ultimately, the proposed early detection, isolation and
explanation of fake news contribute to increase the quality and trustworthiness
of social media contents.
