A Study on Epidemic Information Screening, Prevention and Control of Public Opinion Based on Health and Medical Big Data: A Case Study of COVID-19
Abstract
:1. Introduction
2. Theoretical Analysis
2.1. Health and Medical Big Data Analysis
2.2. Studies Related to COVID-19
2.3. Identification and Analysis of False Information
2.4. Early Warning Analysis of Online Public Opinion
3. Analysis and Construction of the Model of Screening Epidemic Information, as Well as Public Opinion Prevention and Control Based on Health and Medical Big Data
3.1. Construction of the Identification Model for False Epidemic Information
3.1.1. Extraction of Identification Features
- (1)
- The features of the information subject extracted from the coarse granularity level are used to determine whether the information is relevant to the current epidemic theme.
- (2)
- Text structure features extracted from the fine granularity level are used to determine whether the information is repetitive, irrelevant, or advertising.
- (3)
- The emotional polarity extracted from the emotional orientation level is used to judge whether the information is overly complimentary or belittling.
- (4)
- The features of publisher behavior extracted from the of publisher behavior level are used to judge whether the publisher is an authoritative person.
- (5)
- The adjustment features extracted from the historical release of false information of a publisher are used to judge the possibility of the publisher releasing false information.
3.1.2. Analysis and Calculation of the Identification Features
3.1.3. Identification of False Epidemic Information
3.2. The Establishment of an Epidemic Information Public Opinion Early Warning Model
3.2.1. Process Analysis of Epidemic Information Public Opinion Early Warning
3.2.2. The Establishment of an Early Warning Indicator System
- (1)
- Early warning indicators should be representative, which can reflect the basic features of relevant indicators.
- (2)
- Early warning indicators should be quantitative and can be numerically calculated according to big data analysis technology.
- (3)
- Early warning indicators should have relevance, which can reflect the overall situation of early warning, based on the correlation between each early warning indicator.
- (4)
- Early warning indicators should be forward-looking, reflecting the changes of a public opinion trends in the future, and benefit from earlier deployment.
4. Empirical Analysis of COVID-19 Data Based on Big Data Technology
4.1. Data Collection of Epidemic Information
4.2. Identification of Reliable Sources of Epidemic Data
4.3. Feature Extraction and Identification of False Epidemic Information
4.4. Visualization of Epidemic Information Public Opinion Early Warning
4.5. Evaluation of the Model Performance
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Feature Level | Feature Number | Feature Identification |
---|---|---|
Coarse granularity | F1 | Subject relevance |
Fine granularity level | F2 | Number of feature words |
F3 | Number of clauses | |
F4 | Effective length of information | |
Emotional orientation | F5 | Emotional intensity |
Publisher behavior | F6 | Publisher’s name |
F7 | Level of publisher | |
F8 | Amount of information published by the publisher | |
Regulatory feature | F9 | The release of historical false information by the publisher |
Indicator Hierarchy | Indicator Serial Number | Indicator Influencing Factors | Indicator Description |
---|---|---|---|
Macro level | I1 | Change rate of epidemic information quantity | This indicator reflects the changing trend of the amount of epidemic information per unit of time. If the change rate is positive, this indicates that the number of people paying attention to the epidemic continues to increase; if the change rate is negative, this indicates that the number of people paying attention to the epidemic continues to decrease. |
I2 | Regional coverage rate of epidemic information | This indicator reflects the regional distribution involved in the epidemic information. If the coverage rate is high, this indicates that the epidemic situation is a concern in many regions. | |
Micro level | I3 | Emotional tendency of epidemic information | This indicator reflects the public’s attitude toward the epidemic situation. If the emotional tendency continues to increase, this indicates that the public has a positive attitude toward the epidemic situation. |
I4 | Concentration degree of epidemic information subject | This indicator reflects the extent to which some subjects of the epidemic are a concern. If the concentration is high, this means that most people are more concerned about some aspect of the epidemic situation. | |
I5 | New subject regarding epidemic information | This indicator reflects a subject of epidemic information that has not been a concern before. If a new subject emerges, this means that the epidemic has changed and attracted wide public attention. |
ID | URL |
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6 | https://new.qq.com/omn/20200325/20200325A0C3ML00.html |
32 | http://dy.163.com/v2/article/detail/F8IH17IT0514WPGB.html |
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ID | URL |
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4 | Will the spread of COVID-19 become “prolonged”... |
156 | The COVID-19 pandemic, scientists have imagined five ways it could end ... |
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Li, J.; Ma, Y.; Xu, X.; Pei, J.; He, Y. A Study on Epidemic Information Screening, Prevention and Control of Public Opinion Based on Health and Medical Big Data: A Case Study of COVID-19. Int. J. Environ. Res. Public Health 2022, 19, 9819. https://doi.org/10.3390/ijerph19169819
Li J, Ma Y, Xu X, Pei J, He Y. A Study on Epidemic Information Screening, Prevention and Control of Public Opinion Based on Health and Medical Big Data: A Case Study of COVID-19. International Journal of Environmental Research and Public Health. 2022; 19(16):9819. https://doi.org/10.3390/ijerph19169819
Chicago/Turabian StyleLi, Jinhai, Yunlei Ma, Xinglong Xu, Jiaming Pei, and Youshi He. 2022. "A Study on Epidemic Information Screening, Prevention and Control of Public Opinion Based on Health and Medical Big Data: A Case Study of COVID-19" International Journal of Environmental Research and Public Health 19, no. 16: 9819. https://doi.org/10.3390/ijerph19169819