Exposing and explaining fake news on-the-fly

IF 4.3 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Francisco de Arriba-Pérez, Silvia García-Méndez, Fátima Leal, Benedita Malheiro, Juan Carlos Burguillo
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引用次数: 0

Abstract

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.

Abstract Image

即时揭露和解释假新闻
社交媒体平台能够快速传播和消费信息。然而,无论共享数据是否可靠,用户都会即时消费这些内容。因此,后一种众包模式很容易受到操纵。本作品提出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有监督的机器学习方法与在线创建的词库相结合。利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进行剖析。可解释的分类机制可在仪表板上显示分类所选特征和预测置信度。拟议解决方案的性能已通过 Twitter 的真实数据集进行了验证,结果达到了 80% 的准确率和宏观 F-measure。该提案是首个联合提供数据流处理、剖析、分类和可解释性的方案。最终,假新闻的早期检测、隔离和解释有助于提高社交媒体内容的质量和可信度。
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来源期刊
Machine Learning
Machine Learning 工程技术-计算机:人工智能
CiteScore
11.00
自引率
2.70%
发文量
162
审稿时长
3 months
期刊介绍: Machine Learning serves as a global platform dedicated to computational approaches in learning. The journal reports substantial findings on diverse learning methods applied to various problems, offering support through empirical studies, theoretical analysis, or connections to psychological phenomena. It demonstrates the application of learning methods to solve significant problems and aims to enhance the conduct of machine learning research with a focus on verifiable and replicable evidence in published papers.
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