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.

社交媒体平台使信息能够快速传播和消费。然而，无论共享数据的可靠性如何，用户都会立即消费此类内容。因此，后一种众包模式容易受到操纵。这项工作提供了一种可解释的在线分类方法来实时识别假新闻。所提出的方法将无监督和监督机器学习方法与在线创建的词汇相结合。该分析是使用自然语言处理技术使用基于创建者、内容和上下文的特征构建的。可解释的分类机制在仪表板中显示为分类选择的特征和预测置信度。所提出的解决方案的性能已通过 Twitter 的真实数据集进行了验证，结果达到 80% 的准确度和宏观F测量。该提案是第一个联合提供数据流处理、分析、分类和可解释性的提案。最终，对假新闻的早期发现、隔离和解释有助于提高社交媒体内容的质量和可信度。