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The Journal of Supercomputing

Erschienen in:

01.04.2021

A two-step rumor detection model based on the supernetwork theory about Weibo

verfasst von: Xuefan Dong, Ying Lian, Yuxue Chi, Xianyi Tang, Yijun Liu

Erschienen in: The Journal of Supercomputing | Ausgabe 10/2021

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Abstract

Based on the supernetwork theory, a two-step rumor detection model was proposed. The first step was the classification of users on the basis of user-based features. In the second step, non-user-based features, including psychology-based features, content-based features, and parts of supernetwork-based features, were used to detect rumors posted by different types of users. Four machine learning methods, namely, Naive Bayes, Neural Network, Support Vector Machine, and Logistic Regression, were applied to train the classifier. Four real cases and several assessment metrics were employed to verify the effectiveness of the proposed model. Performance of the model regarding early rumor detection was also evaluated by separating the datasets according to the posting time of posts. Results showed that this model exhibited better performance in rumor detection compared to five benchmark models, mainly owing to the application of the supernetwork theory and the two-step mechanism.

Vorheriger Artikel iHRNL: Iterative Hessian-based manifold regularization mechanism for localization in WSN

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Titel: A two-step rumor detection model based on the supernetwork theory about Weibo
verfasst von: Xuefan Dong
Ying Lian
Yuxue Chi
Xianyi Tang
Yijun Liu
Publikationsdatum: 01.04.2021
Verlag: Springer US
Erschienen in: The Journal of Supercomputing / Ausgabe 10/2021
Print ISSN: 0920-8542
Elektronische ISSN: 1573-0484
DOI: https://doi.org/10.1007/s11227-021-03748-x

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