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| Misinformation Identification Method by Automatic Iterative Clustering Data Set for Training |
| Zhang Junsheng1, Sun Xiaoping2, Liu Zhihui1 |
1.Institute of Scientific and Technical Information of China, Beijing 100038
2.KL-IIP, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190 |
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Abstract With increasing proliferation of misinformation on the Internet, automatic identification of misinformation has become an urgent need for information governance. Misinformation on the Internet is constantly generated with new events, thereby resulting in the need for iterations and updates in the machine learning model to identify such misinformation. A new training data set should be constructed for each iteration update, so that the new misinformation can be reflected in the training set. Therefore, this study proposes a misinformation recognition method of dynamically and iteratively updating the training set to build a machine learning model, and iteratively clustering the misinformation data set based on kernel density estimation. In each cluster, training set and test set samples are selected to construct the corresponding classifier training data set and test data set; this enables the samples of new events to be reflected in the training set. The experimental results show that the misinformation classifier trained by the iterative clustering method based on kernel density estimation can significantly improve the accuracy of false information classification compared with the random data set division strategy.
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Received: 28 October 2021
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