Dong Xuefan, Lian Ying, Chi Yuxue, Tang Xianyi, Liu Yijun
Research Base of Beijing Modern Manufacturing Development, Beijing University of Technology, Beijing, 100124 People's Republic of China.
College of Economics and Management, Beijing University of Technology, Beijing, 100124 People's Republic of China.
J Supercomput. 2021;77(10):12050-12074. doi: 10.1007/s11227-021-03748-x. Epub 2021 Apr 1.
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.
基于超网络理论，提出了一种两步谣言检测模型。第一步是基于用户特征对用户进行分类。第二步，使用包括基于心理的特征、基于内容的特征以及部分基于超网络的特征等非用户特征来检测不同类型用户发布的谣言。应用朴素贝叶斯、神经网络、支持向量机和逻辑回归四种机器学习方法训练分类器。采用四个实际案例和几个评估指标来验证所提模型的有效性。还通过根据帖子发布时间对数据集进行划分来评估模型在早期谣言检测方面的性能。结果表明，与五个基准模型相比，该模型在谣言检测方面表现出更好的性能，这主要归功于超网络理论和两步机制的应用。
