# Cross-Domain Transfer Learning Prediction of COVID-19 Popular Topics Based on Knowledge Graph
## 4 Transfer Learning Model
### Experiment  
Based on previous research results and combined with the characteristics of COVID-19 emergencies [29], this paper divides the development of epidemic public opinion into six stages: incubation period, growth period, outbreak period, fluctuation period, decline period, and long tail period, as is depicted in Figure 5. Additionally, in order to more accurately reflect the topic content in each period, according to the statistical analysis results of the life cycle stages, this paper divided data into time slices in the granularity of one week (7 days). The first 70% of the data in the two domains was selected as the training set, including 14 time slices (T0, T1,..., T14) from 25 December 2019 to 7 April 2020). The last 30% of the data was selected as the testing set, including 7 time slices (T15, T16,..., T21) from 8 April 2020 to 31 May 2020. The data were then allocated to the corresponding time slice, and the text in each time slice was processed in turn and the subject extracted.  
Figure 5: Stage division of life cycle of Weibo and TikTok platform events.  
The data used in this experiment are the data from the Weibo and TikTok platforms after data pretreatment. This paper takes the LDA model processing of source domain and target domain data on T1 time slice as an example to illustrate. Data on other time slices were processed according to the similar LDA model mentioned above.  
Through Gensim's LDA Model method, the subject number range was set as [2; 20], and the subject consistency scores of LDA models in the source domain and target domain under different subject numbers were obtained, as shown in Figure 6. The best number of subjects for the source domain T1 time slice data was obtained as 16, and the model's perplexity is \(-6.9\). For subjects in the target domain, the optimal number was 12, with the model's perplexity equal to \(-5.27\).  
Then we set the number of subjects in the source domain to 16, the number of subjects in the target domain to 12, and reserved 10 keywords for each subject. Check the subject results extracted from the source domain and target domain in Figure 7.  
Figure 6: This is the subject consistency of T1 time slice data in the two domains: (**a**) is the subject consistency score of source domain T1 time slice data; (**b**) is the subject consistency score of target domain T1 time slice data.  
Figure 7: This is the subject classification of the source domain and target domain T1 time slice data: The T1 time slice of the source domain has 16 subjects; The T1 time slice of the target domain has 12 subjects. There are 10 subjects in the union of the two domains.  
Each subject is composed of multiple words, and the same word may be contained in multiple subjects. For example, the 10 key words of the EP subject include \"prevention and control, COVID-19, epidemic, combat, Novel Coronavirus, pneumonia, gather, disinfection, and Wuhan\" (note: original data keywords are Chinese; this is translated data). Based on the above analysis, this paper is mainly divided into 10 subjects: epidemic condition (EC), data reporting (DR), epidemic prevention (EP), epidemic impact (EI), charity donations (CD), scientific research and popularization (SRP), people's stories (PS), rumor clarification (RC), emotional subjects (ES), and other subjects (OS).