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| Comparison and Improvement of Health Misinformation Identification Methods in WeChat Official Account Articles |
| Wang Lei1, Song Shijie2, Zhu Qinghua1 |
1.School of Information Management, Nanjing University, Nanjing 210023
2.Business School, Hohai University, Nanjing 211000 |
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Abstract Recently, the proliferation of health misinformation in WeChat official account articles has impacted users’ access to health knowledge and decreased their ability to make informed health decisions. To suppress the dissemination of health misinformation, it is necessary to study methods of automatically identifying and detecting health misinformation. This study uses samples from two sources: health articles published by authority accounts (e.g., “Science China,” “Ding Xiang Doctor,” and other governmental accounts) and articles containing health misinformation that have been labeled. Health misinformation is identified through the steps of word segmentation, stop word removal, syntax feature extraction, and text classification. We selected the best classifier through the comparison of accuracy, precision, recall, training time, and other performance-related indicators. Moreover, to solve the problems of polysemy and synonyms in text classification, this paper used Latent Dirichlet Allocation (LDA) topic analysis to extract the semantic features of the text and then proposed a feature extraction method based on “syntax plus semantics.” The experiments suggest that our proposed new method had better performance over methods based on semantic feature extraction and other prior models. By proposing a novel method for identifying health misinformation in WeChat official account articles, this study may have practical implications for online health misinformation governance.
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Received: 23 December 2021
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