Abstract
The wanton dissemination of network pseudohealth information has brought great harm to people’s health, life, and property. It is important to detect and identify network pseudohealth information. Based on this, this paper defines the concepts of pseudohealth information, data block, and data block integration, designs an architecture that combines the latent Dirichlet allocation (LDA) algorithm and data block update integration, and proposes the combination algorithm model. In addition, crawler technology is used to crawl the pseudohealth information transmitted on the Sina Weibo platform during the “epidemic situation” from February to March 2020 for the simulation test on the experimental case dataset. The research results show that (1) the LDA model can deeply mine the semantic information of network pseudohealth information, obtain the features of document-topic distribution, and classify and train topic features as input variables; (2) the dataset partitioning method can effectively block data according to the text attributes and class labels of network pseudohealth information and can accurately classify and integrate the block data through the data block reintegration method; and (3) considering that the combination model has certain limitations on the detection of network pseudohealth information, the support vector machine (SVM) model can extract the granularity content of data blocks in pseudohealth information in real time, thus greatly improving the recognition performance of the combination model.
Highlights
At present, pneumonia caused by the new coronavirus has been effectively controlled nationwide, but the panic and fear caused by it are making people nervous
Pseudohealth information can be classified from different aspects, the existing methods are mainly single classifiers or batch processing, which result in either the classification cannot be effective or the recognition accuracy not being high. rough the research on pseudohealth information, this paper aims to help people distinguish pseudohealth information and improve their health information literacy, fundamentally improving the quality of network health information and purifying the network health information environment
This paper proposes an integrated combination of the latent Dirichlet allocation (LDA) algorithm and data partitioning and accurate update
Summary
Pneumonia caused by the new coronavirus has been effectively controlled nationwide, but the panic and fear caused by it are making people nervous. People are attempting to find various effective methods for improving their immunity to resist virus invasion and prevent virus infection by the new coronavirus. Under this background, some people take advantage of the panic mentality of the public to produce and disseminate a large amount of pseudohealth information on the Internet in the name of health. Pseudohealth information spreads unscrupulously in rural areas, resulting in a series of serious consequences. In recent years, there have been activities to promote fake health care products in rural areas
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