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Analysis and Classification of Fake News Using Sequential Pattern Mining

BIG DATA MINING AND ANALYTICS 2024年第3期7卷 942-963页

作者： Nawaz, M. Zohaib Nawaz, M. Saqib Fournier-Viger, Philippe He, Yulin Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen 518060 Peoples R China Guangdong Lab Artificial Intelligence & Digital Ec Shenzhen 518107 Peoples R China Univ Sargodha Fac Comp & Informat Technol Dept Comp Sci Sargodha 40100 Pakistan

Disinformation, often known as fake news, is a major issue that has received a lot of attention lately. Many researchers have proposed effective means of detecting and addressing it. Current machine and deep learning based methodologies for classification/detection of fake news are content-based, network (propagation) based, or multimodal methods that combine both textual and visual information. We introduce here a framework, called FNACSPM, based on sequential pattern mining (SPM), for fake news analysis and classification. In this framework, six publicly available datasets, containing a diverse range of fake and real news, and their combination, are first transformed into a proper format. Then, algorithms for SPM are applied to the transformed datasets to extract frequent patterns (and rules) of words, phrases, or linguistic features. The obtained patterns capture distinctive characteristics associated with fake or real news content, providing valuable insights into the underlying structures and commonalities of misinformation. Subsequently, the discovered frequent patterns are used as features for fake news classification. This framework is evaluated with eight classifiers, and their performance is assessed with various metrics. Extensive experiments were performed and obtained results show that FNACSPM outperformed other state-of-the-art approaches for fake news classification, and that it expedites the classification task with high accuracy.

关键词： Measurement Accuracy Linguistics Feature extraction Real-time systems Classification algorithms Data mining disinformation fake news sequential pattern mining (SPM) frequent patterns classification

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Topology:一个快速的频繁连通子图的挖掘算法

Topology:一个快速的频繁连通子图的挖掘算法

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第二十届全国数据库学术会议

作者：吴颖华周皓峰袁晴晴洪铭胜汪卫施伯乐复旦大学计算机与信息技术系

1引言关联规则挖掘是数据挖掘的关键任务。应用领域很多,如Web挖掘,生物信息学,购物篮分析等。抽取频繁模式是关联规则挖掘的必要阶段。从Apriori,DHP,DIC到FP-Growth,大部分工作都

关键词： Data mining frequent patterns frequent connected subgraph Topology

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