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.

该研究提出了一种可解释和在线的分类方法，结合无监督和有监督的机器学习方法，使用自然语言处理技术基于创建者、内容和上下文的特征，识别和解释社交媒体中的虚假新闻，并通过数据流处理提供实时的早期检测、隔离和解释，从而提高社交媒体内容的质量和可信度。