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History & Philosophy of Medicine doi: 10.12032/HPM20210404031
Analysis of microblog public opinion characteristics on traditional
Chinese medicine against COVID-19 based on deep learning
Shi-Pian Li1, Xue-Meng Cai1, Cheng Chen1, Ze-Lin Wei2, Wen-Zong Zhang3, Dai-Le Zhang1, Yong-Ming Guo1, Xin-Ju Li1 *
1
Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China. 2Central China Normal University, Wuhan
430077, China. 3Beijing University of Technology, Beijing 100081, China.
*Corresponding to: Xin-Ju Li. Tianjin University of Traditional Chinese Medicine, No. 10, Poyanghu Road, West Area,
Tuanbo New Town, Jinghai District, Tianjin 301617, China. E-mail: mars402498971@126.com.
Abstract
The opinion research on traditional Chinese medicine during the coronavirus disease 2019 (COVID-19) pandemic
on microblog, a social network, took into account the national people’s fight against COVID-19 — the research
background — the strength of traditional Chinese medicine during the pandemic — the research topic — and the
public opinion — the research object. The timeline was divided into three stages according to the overall heat
change. In order to explore and compare people’s emotion and topics of concern on traditional Chinese medicine
during the different stages of the pandemic, deep learning analysis methods such as emotional analysis and Latent
Dirichlet Allocation analysis were used. This study found that the public’s positive “emotional composition” on
traditional Chinese medicine significantly improved within the timeline, while the public’s autonomy was enhanced
and the overall public opinion started to show an increased trend.
Keywords: Deep learning, COVID-19, Public opinion analysis, Traditional Chinese medicine
Competing interests:
The authors declare no conflicts of interest.
Acknowledgments:
The authors did not receive any funding for this study.
Abbreviation:
COVID-19, coronavirus disease 2019; TCM, traditional Chinese medicine; LSTM, long-term and short-term
memory network; LDA, latent dirichlet allocation.
Citation:
Li SP, Cai XM, Chen C, et al. Analysis of microblog public opinion characteristics on traditional Chinese
medicine against COVID-19 based on deep learning. Hist Philos Med. 2021;3(2):9. doi:
10.12032/HPM20210404031.
Executive editor: Shan-Shan Lin.
Submitted: 16 March 2021, Accepted: 04 April 2021, Online: 16 April 2021.
© 2021 By Authors. Published by TMR Publishing Group Limited. This is an open access article under the CC-BY
license (http://creativecommons.org/licenses/BY/4.0/).
Submit a manuscript: https://www.tmrjournals.com/hpm 1
doi: 10.12032/HPM20210404031 REVIEW
academic community regarded the protest against
Background NATO bombing incident in People’s Network Forum
in 1999 as the beginning of public opinion’s
During the coronavirus disease 2019 (COVID-19) effectively entering into the Chinese society. We
pandemic, people responded to the calls to refrain searched the general database with “ 舆 情 (public
going outdoors. Consequently, the time spent on the opinion)” as a key word, in the annual publication of
Internet has greatly increased. By last December, the HowNet (Figure 1). The rise of public opinion research
internet penetration rate in China had reached 70.4%, is only about ten years old. Nowadays, public opinion
and the number of Internet users was approximately research mainly focuses on public opinion monitoring,
989 million [1]. At the peak of the pandemic, the analysis, and guidance. Through the analysis of public
average number of hours of internet use per week was opinion literature on HowNet, preliminary results were
up to 30.8 hours, which was significantly higher than obtained.
in other periods. During this time, people paid
attention to the progress of the front-line pandemic Data sources and engineering characteristics
work. For example, hot topics appeared online such as In this paper, we crawled all 198,928 text data with
the construction of the Wuhan Huoshenshan Hospital “ 中 医 药 (TCM)” as the keyword in a microblog.
and the Wuhan Leishenshan Hospital. The internet Firstly, we crawled the microblog data through the
public opinion on public health also reached an scratch distributed crawler framework and configured
unprecedented dimension. The decentralized trend of the agent to solve the anti-crawling mechanism of
online social communication has contributed to the microblog. Then, we preprocessed the data, deleted the
actual degree of freedom of public opinion. stop words (https://github.com/goto456/stopwords.),
Particularly, the internet has become a new approach used the term frequency–inverse document frequency
to express opinion. Consequently, the internet enabled (TF-IDF) [4] algorithm to process the text data, and
research on the public opinion on epidemic-related finally obtained the matrix expression containing all
content. the text information.
Because of the lack of knowledge on Chinese
traditional culture, the public has low awareness of Text sentiment analysis
traditional Chinese medicine (TCM) and easily Text sentiment analysis is a process of analysis,
misinterprets it. Since 13th five-year, the Communist processing, induction, and reasoning subjective text
Party of China Central Committee with Comrade Xi with emotional color [5]. This section aimed to make
Jinping gave great importance to the development of use of the long-term and short-term memory network
TCM. In the opinion of the Communist Party of China (LSTM) in deep learning technology [6]. Based on the
Central Committee and the State Council on promoting emotional analysis of the text, LSTM network are a
the inheritance, innovation, and development of TCM, variant of recurrent neural network. Recurrent neural
researchers should promote the benefits of TCM network can only have short-term memory because of
culture through media, strengthen and standardize the the gradient disappearance. LSTM network combines
dissemination and popularization of knowledge about short-term memory with long-term memory through
prevention and treatment of diseases in TCM, and subtle gate control, and solves the problem of gradient
create a social atmosphere on TCM that people cherish, disappearance to a certain extent. At present, it showed
love, and support [2]. During the fight against a good performance in solving the problems of time
COVID-19, there was approximately 5,000 Chinese series, natural language processing, and speech
medical staff. For patients with COVID-19, the recognition.
utilization rate of Chinese medicines was over 92% [3], In this paper, we choose the popular microblog
and the effective rate of the confirmed cases in Hubei comment emotion data set as the training set of the
was over 90%. TCM has played an irreplaceable role neural network. The data set had three types of tags:
in the fight against this pandemic. The search on positive, negative, and neutral. In this model, the input
internet’s public opinion of TCM can complement the layer node of LSTM network was set to 50. Because
research systems on public opinion, providing a the final prediction result could be of three types, the
support for policy implementation and referencing output layer node was 3. The dropout layer was added
TCM as essential in improving health during the to prevent over fitting. By adjusting parameters and
pandemic. comparing the results, the number of nodes in the
hidden layer was 16. In the process of building the
Methods model, MSE, meaning mean square error, was chosen
as the loss function, tanh was selected as the activation
Literature research method function in the hidden layer, and softmax was
At the end of the 18th century, Rousseau had put preferred as the output layer. In order to find the best
forward the concept of “public opinion”. Domestic balance between memory efficiency and memory
public opinion research started relatively late. The capacity, RTX Titan graphics card was selected.
2 Submit a manuscript: https://www.tmrjournals.com/hpm
History & Philosophy of Medicine doi: 10.12032/HPM20210404031
Figure 1 Trends of HowNet public opinion literature
Because of the low number of parameters, there was words in each text is as follows.
no need to consider the use of video memory, so batch T
was selected. When the loss of training was less than P ( wi ) P ( wi zi )P ( zi j )
1e-5, the training was stopped. It was found that the j 1
training had stopped when the iteration was of about
four times. There were 14,795 complete training In this paper, we used Gensim to build a topic
parameters with a final training accuracy of 94%. The analysis model and PyLDAvis to visualize the model.
model was saved and outputted as the model weight. Finally, we calculated the text confusion degree to
Finally, all the parameters were used to train the neural determine the topic parameters and to evaluate the
network, and the test data was added to the final model. Confusion degree meant that the number of
model. document topics generated by the training model is
uncertain. Different number of topics will change the
Latent dirichlet allocation confusion degree, being lower when the document
clustering effect is better.
Latent dirichlet allocation (LDA) is a topic analysis
method of mining text topics using a probabilistic Basis of stage division
model [7]. Based on the maximum likelihood method According to the latest statistics of the Chinese internet
and generative model, LDA reduces the dimension of network information center, microblog is the third
high-dimensional text data to a lower dimensional largest social networking platform, which is second
space. On this basis, if LDA is used prior distribution, only to WeChat’s circles of friends and QQ space. Due
it forms a naive Bayesian model of article-topic-single to its information openness, it is more approachable to
word. Finally, LDA finds the semantic structure and the development of data mining. Therefore, microblog
mines articles by calculating their probability. Each was used as the data source platform in this study.
text can be expressed as the probability distribution P Baidu is a high usage search engine, with a daily user
(z) of a series of topics, and each topic is the activity of nearly 200 million people. The research
probability distribution P (w|z) of all words in the period was divided according to Baidu Index and
vocabulary. Therefore, the probability distribution of microblog.
Submit a manuscript: https://www.tmrjournals.com/hpm 3
doi: 10.12032/HPM20210404031 REVIEW
Baidu search index (Figure 2) calculated the weight Combined with the aforementioned topics, three
of each keyword search frequency based on the time periods were selected for the study. (1) Heating
number of internet users’ keyword search in Baidu. up period of TCM against COVID-19 public opinion:
This study selected Baidu search index as a reference, from December 29th, 2019 (first case in Jinyintan
with “ 中 医 (Chinese medicine)”, “ 中 药 (Chinese Hospital) to February 14th, 2020 (national medical
herbs)”, and “中医药 (TCM)” as keywords. The peak team of TCM will be stationed in Jiangxia Shelter
of attention was between mid-February to mid-March Hospital) and obtained 52,221 text data. (2) Constant
(Figure 3). Microblog hot search real-time launched 50 temperature period of TCM against COVID-19 public
topics for ranking according to the user search volume. opinion: from February 14th, 2020 to March 23rd,
In the important stage of COVID-19, 32 microblogs 2020 (clinical shows that the total effective rate of
were crawled with “ 中 医 (Chinese medicine)” and TCM is more than 90%), and obtained 113,203 text
“ 中 药 (Chinese herbs)” as keywords to form topic data. (3) Cooling down period of TCM against
heat bubble chart (Figure 3). The heat change trend COVID-19 public opinion: from March 23rd, 2020 to
was consistent with Baidu Index. As this study took April 17th, 2020 (nearly 93% of local cases in
microblog as the main object, it was divided into Shanghai were treated with TCM) and obtained 33,504
stages based on the time points of major turning text data.
events.
Figure 2 Keyword heat change chart (source: Baidu Index)
Figure 3 Microblog topic popularity bubble chart (source: microblog hot search)
4 Submit a manuscript: https://www.tmrjournals.com/hpm
History & Philosophy of Medicine doi: 10.12032/HPM20210404031 Results Literature research results Extensive research on public opinion of COVID-19 can be found mainly focusing on traditional statistics or deep learning analysis methods. According to the different components of public opinion participation, Yang Xiuzhang et al. [8] carried out public opinion research based on a topic platform, Wang Nan [9] and Ning Zhonghua et al. [10] carried out emotional analysis and empirical research on news media, and Liu Zhibin et al. [11] analyzed public opinion characteristics of university audience. Internationally, Richard J. et al. [12] have conducted an analysis on Twitter. In the theme of TCM against COVID-19, the article “observation and research on word-of-mouth of Figure 4 Sentiment analysis during the “heating traditional Chinese medicines from the perspective of up” period internet in 2020” of people’s network data monitoring center [13] confirmed the outstanding contribution of word-of-mouth to the overall importance of TCM during the COVID-19 pandemic. Jiang Jiebing [14] and Zhao Yang [15] respectively studied the public opinion of two opposite events of promulgation of the TCM law and the Shuanghuanglian incident. Taking into account TCM as an example, Gai yun [16] confirmed the feasibility of deep learning analysis method in this field. LDA technology was used by Li Yanjiang [17] to mine microblog topics in the COVID-19 front metaphase, which confirmed the feasibility of this research method and the accuracy of preliminary data conclusions. To the best of our knowledge, there has been no international research made on this specific topic. Compared with other research topics about public opinion during COVID-19 pandemic, there is less literature available on TCM. Figure 5 Sentiment analysis during the “constant Similarly, research on long-term comparative changes temperature” period during this time-frame is scarce. Three-stage emotion analysis This paper selected microblog text data from December 29th, 2019 to April 17th, 2020, and obtained the three-stage LSTM sentiment analysis results (Figure 4–6). In these three stages, positive emotions have increase significantly, while negative emotions decreased significantly. Before and after the COVID-19 pandemics, the public’s favor for TCM increased significantly, and their attitude changed. Compared with the hot topics of TCM (Table 1), in the “heating up” period, the action of TCM against COVID-19 presented unclear data, less celebrity effect, and insufficient depth of intervention diagnosis and treatment. Contrarily, in the constant temperature period, changes were noticed in considerable data, increased depth of intervention, and significant effect. Figure 6 Sentiment analysis during the “cooling In the cooling period, publicity influenced the celebrity temperature” period effect to optimize the influence of TCM. Submit a manuscript: https://www.tmrjournals.com/hpm 5
doi: 10.12032/HPM20210404031 REVIEW
LDA correlated to the similarity between two topics. On the
Topic number selection. In this study, LDA model right side of the figure, the details of the
was used for topic analysis, and confusion curves were high-frequency words of the current topic can be
obtained for each stage. Taking the heating up period observed. The results of this study were divided into
as an example (Figure 7), in order to ensure the central theme and marginal theme, based on the
intuitive and practical significance of data analysis, we proportion of topics and topic similarity (Tables 2 and
selected six topics with relatively low confusion 3).
degree and appropriate number of topics for analysis, Change analysis of central theme. The analysis
among the three stages. results show the frequency distribution of LDA topic
Visualization of LDA topic analysis results. The data words. Words not relevant to the topic identification
analysis was visualized using PyLDAvis module were deleted, such as related words, verbs, and
(Figure 8–10). On the left side of the figure, the topic quantifiers. High-frequency words were selected for
mutual view diagram can be seen. The size of the list display (Table 2). According to the corresponding
circle is positively correlated to the proportion of this time, the original microblog was retrieved, and the
topic, and the distance of the circle is negatively topic name summarized.
Table 1 Microblog hot topics
Order Date Hot topics Topic degree
Two cases of new pneumonia in Beijing cured by symptomatic and
1 2020/01/25 43,250
TCM.
The first batch of patients treated with TCM in Jinyintan Hospital
2 2020/02/03 19,135
were discharged.
3 2020/02/6 Progress in screening effective prescriptions of TCM. 32,981
The effect of TCM intervention in early stage of disease is
4 2020/02/11 114,353
obvious.
Promoting the deep intervention of TCM in diagnosis and
5 2020/02/13 67,563
treatment.
National medical team of TCM will be stationed in Jiangxia
6 2020/02/14 228,994
Shelter Hospital.
7 2020/02/14 Visit Wuhan Jiangxia TCM Shelter Hospital. 119,779
After taking Chinese medicine novel coronavirus pneumonia
8 2020/02/14 160,722
patients’ tension is relieved.
9 2020/02/14 TCM can reduce the transformation from severe to critical illness. 182,663
The participation rate of TCM in Hubei confirmed cases reached
10 2020/02/15 173,298
75%.
3 national medical teams of TCM have been dispatched with 2,220
11 2020/02/15 159,185
people.
More than half of the confirmed cases in Hubei Province were
12 2020/02/15 177,295
treated with TCM.
13 2020/02/15 Wang Hesheng’s early use of traditional Chinese medicine. 135,237
14 2020/02/16 How effective TCM on critically ill patients are? 121,689
Beijing novel coronavirus pneumonia participation rate of Chinese
15 2020/02/16 123,614
medicine is 90%.
Discharge of two patients treated with only Chinese medicine in
16 2020/2/16 35,925
Jiangxi Province
Recommended prescription of TCM for epidemic prevention in
17 2020/02/19 152,665
Zhejiang Province.
18 2020/02/20 A day for a doctor of TCM in Huoshenshan Hospital. 224,923
Beijing novel coronavirus pneumonia total effective rate of TCM
19 2020/02/24 189,351
is 92%.
Suggestions on rehabilitation of novel coronavirus pneumonia
20 2020/02/25 257,989
during rehabilitation period.
21 2020/02/26 23 patients discharged from the first TCM shelter hospital. 239,234
22 2020/03/07 The first designated hospital of TCM to clear patients in Wuhan. 237,462
6 Submit a manuscript: https://www.tmrjournals.com/hpm
History & Philosophy of Medicine doi: 10.12032/HPM20210404031
Table 1 Microblog hot topics (Continued)
Order Date Hot topics Topic degree
TCM treatment experience is the highlight of Chinese
23 2020/03/16 212,577
anti-COVID-19 program.
24 2020/03/17 The participation rate of TCM outside Hubei was 96.37%. 180,834
Clinical shows that the total effective rate of TCM is more than
25 2020/03/23 169,881
90%.
26 2020/03/23 More than 4900 Chinese medicine personnel rush to Hubei. 167,779
No need to worry that westerners would not accept TCM
27 2020/03/23 185,521
treatment.
Centralized isolation and common use of TCM prevented the
28 2020/03/23 24,688
spread of the epidemic.
29 2020/03/23 Zhang Boli said that promotion of TCM depends on effect. 258,538
China is willing to provide assistance in TCM to countries and
30 2020/03/23 46,268
regions in need.
31 2020/04/05 Zhang Wenhong talks about anti-epidemic of TCM. 211,196
32 2020/04/17 Nearly 93% of local cases in Shanghai were treated with TCM. 146,657
TCM, traditional Chinese medicine.
Figure 7 Confusion degree curve of LDA model. LDA, latent dirichlet allocation.
Figure 8 Heating up period high proportion theme PyLDAvis module
Submit a manuscript: https://www.tmrjournals.com/hpm 7
doi: 10.12032/HPM20210404031 REVIEW
Figure 9 Constant temperature period high proportion theme PyLDAvis module
Figure 10 Cooling period high proportion theme PyLDAvis module
8 Submit a manuscript: https://www.tmrjournals.com/hpm
History & Philosophy of Medicine doi: 10.12032/HPM20210404031
Table 2 LDA topic analysis with the change of the central topic
Topic (%) High frequency words (in order of frequency)
肺炎 (pneumonia), 新型 (new type), 冠状病毒 (coronavirus),
疫 情 (epidemic), 中 医 药 (TCM), 感 染 (infection), 救 治
Prevention and control plan
(treatment), 防 控 (prevention and control), 方 案 (program),
(25.9%)
国 家 (country), 诊 疗 (diagnosis and treatment), 专 家
(expert), 预防 (prevention), 发布 (release)
治 疗 (treatment), 患 者 (patients), 中 医 药 , 肺 炎 , 中 药
(Chinese medicine), 症 状 (Symptoms), 使 用 (use), 临 床
Heating up Prevention of TCM (clinical), 方 剂 (prescription), 预 防 , 双 黄 连
period (23.9%) (Shuanghuanglian), 汤 (decoction), 颗 粒 (granule), 有 效
(effective), 药 物 (medicine), 清 肺 (Qingfei), 研 究
(research), 排毒 (Paidu), 轻症 (mild disease)
中 医 药 大 学 (University of TCM), 武 汉 (Wuhan), 中 医
(TCM), 微 博 (microblog), 转 发 (forwarding), 月 (month),
Support team (23.8%) 医 院 (hospital), 加 油 (come on), 视 频 (video), 理 由
(reason), 中国 (China), 湖北 (Hubei), 河南 (Henan), 山西
(Shanxi)
疫情, 医院, 武汉, 国家, 新冠, 中国, 肺炎, 中医药大学, 中
Anti COVID-19 line
医, 防控, 医疗 (medical), 抗击 (fight), 天津 (Tianjin), 专
(30.8%)
家组 (expert group)
中医, 治疗, 中药, 瑞德西韦 (Remdesivir), 患者, 研究, 西
Comparison of Chinese and 医 (Western medicine), 美国 (USA), 临床试验 (临床试验),
Constant western medicine (25.8%) 死亡率 (mortality), 病毒 (virus), 基础 (foundation), 张文宏
temperature (Zhang Wenhong), 中国
period
中医药, 临床, 例 (case), 患者, 出院 (leave hospital), 病例,
Praise the curative effect 使 用 , 排 毒 , 清 肺 , 救 治 , 疫 情 , 作 用 (function), 重 症
(15.9%) (Critically ill patients), 治愈 (cure), 莲花清瘟 (Lotus clearing
away pestilence)
治疗, 中医药, 新冠, 疫情, 肺炎, 中医, 国家, 防控, 研究,
Praise the curative effect
时间 (time), 工作 (work), 方面 (aspects), 救治, 医疗, 效
(33.9%)
Cooling 果, 重症 (Critically ill patients)
period 中医药, 院士 (academician), 张伯礼 (Zhang Boli), 使用, 新
Expert effect (12.2%) 冠, 经验 (experience), 肺炎, 抗疫, 作用, 表示 (expression),
疫情, 治疗, 专家, 中国, 发挥 (play)
LDA, latent dirichlet allocation.
Table 3 The results of LDA topic analysis on the change of marginal topic during the three stages
Topic (%) High frequency words (in order of frequency)
医院, 候诊 (Waiting for treatment), 市 (city), 人民 (people),
中 医 医 院 (TCM hospital), 医 科 大 学 (Medical University),
Waiting information
中 心 医 院 (Central Hospital), 第 二 (second), 附 属
(12.6%)
(subsidiary), 第 一 (first), 院 区 (Hospital district), 保 障
(guarantee), 发热 (fever)
Heating up
确诊 (diagnosis), 病例, 隔离 (quarantine), 岁 (age), 就诊
period
(See a doctor), 河南, 武汉, 治疗, 出现 (appear), 男 (man),
Case study (7.5%)
目前 (at present), 年 (year), 症状, 西安市 (Xi'an City), 肺
炎, 病情, 郑州市, 山西
Logistical academies and 例, 临床, 患者, 出院, 排毒, 清肺, 汤, 确诊, 治愈, 学院,
schools (6.3%) 云南, 昆明, 转发, 理由, 中医药, 中医
Submit a manuscript: https://www.tmrjournals.com/hpm 9
doi: 10.12032/HPM20210404031 REVIEW
Table 3 The results of LDA topic analysis on the change of marginal topic during the three stages
(Continued)
Topic (%) High frequency words (in order of frequency)
中医药, 抗疫, 院士, 国际 (international), 中国, 张伯礼, 社
会 (society), 积极 (positive), 评价 (evaluation), 经验, 中医,
专家, 治疗, 意大利 (Italy), 中药 (Chinese herbal medicine),
International effect (12.1%)
亮 点 (Highlights), 中 国 工 程 院 (Chinese Academy of
engineering), 方案 (scheme), 黄璐琦 (Huang Luqi), 仝小林
Constant (Tong Xiaolin), 世界 (world)
temperature
Waiting information
period As last stage
(10.4%)
中医药大学, 湖北, 学院 (college), 大学 (university), 作者
logistical academies and (author), 共 青 团 中 央 (Central Committee of the Communist
schools (4.9%) Youth League), 中医, 团委 (Youth League Committee), 河南
(Henan), 南京 (Nanjing)
中医药, 中医, 抗疫, 国际, 中国, 社会, 积极, 评价
International effect (19.7%) (evaluate), 瑞德西韦, 中药, 美国, 西医, 死亡率 (mortality),
张文宏
中医药大学, 武汉, 湖北, 医院, 中医, 疫情, 地图 (map),
Logistical academies and
附属, 学院, 学生会 (student union), 医疗队 (medical team),
Cooling schools (16.9%)
河南
period
Waiting information
As last stage
(10.4%)
例, 临床, 患者, 出院, 排毒, 清肺, 病例, 确诊, 治愈, 数据
Treatment effect (6.9%) (data), 重症, 症状, 观察 (observation), 芝加哥大学 (The
University of Chicago), 死亡 (death)
LDA, latent dirichlet allocation.
The trend of the central topic throughout the three government advocacy and practitioners’
stages was similar to Chinese medicine against recommendation. The masses lacked the motive force
COVID-19. With the development of anti-pandemic to seek help from TCM. In the second and third stages,
actions, public opinion also presented the following after a large number of data proved the effectiveness of
characteristics. (1) From “supplementary” medical TCM in COVID-19, several people spontaneously
treatment on the battlefield to “necessary” medical praised the TCM on the internet. The enrichment of
treatment. There was no treatment plan of TCM in the propaganda made the prevention and control of
early two editions of COVID-19 pneumonia issued by TCM-based treatments obtain powerful internal
the Chinese state. The early intervention of TCM driving force. (4) From focusing on the effect of
mainly focused on the prevention and treatment. Not simple drugs to trusting the instrumental role of TCM,
until the establishment of Jiangxia Shelter Hospital, as the spiritual core of a “national inheritance”. In the
TCM began to participate as front-line alternatives early stage, the therapeutic effect of TCM was often
against the COVID-19. (2) From the treatment of mild misunderstood as database of specific drug, and
to severe patients, the boundaries created around the finding the “magic medicine” to kill the virus from the
interventional treatment were broken. TCM has always rich Chinese herbal medicine. Under the exaggeration
been regarded as “conditioning medicine” with slow of the media and the extreme interpretation of the
curative effect by the public. In the early stage of the crowd, TCM was used as a self-help tool, but
pandemics, TCM intervention was only applied to mild abandoned after. In the later stage, people’s attention
patients. In the middle stage, the public starts to see to the field of TCM has risen from medical and experts
therapeutic effect of TCM on moderate and severe advice.
patients, which is a milestone for breaking the public’s Accurate and scientific information effectively
first impressions. (3) From policy guidance to public reduces misunderstanding. A report [13] noticed that
spontaneity. In the early stage, the treatment of the focus of public opinion in the early stage was
COVID-19 with TCM mainly came from the divided and antagonistic, so the proportion of positive
10 Submit a manuscript: https://www.tmrjournals.com/hpmHistory & Philosophy of Medicine doi: 10.12032/HPM20210404031
emotions was not high. The fundamental reason behind grasp the mass psychological line.
this was that the spread of rumors and The opinions of media and celebrities should avoid
misunderstanding founded public opinion disputes. In ambiguity and bias. In different periods, media and
the later stage, the scientific and objective information celebrities discuss and publicize TCM. From the
increased the credibility given to TCM and reduced polarization of public opinion in the early stage to the
possible misunderstandings. dominance of positive public opinion in the later stage,
Analysis on the change of marginal theme stage. the public opinion was mainly affected by the
The similarity and discussion degree between the scientific and authentic data on the topic source. Media
marginal topic and the central theme were relatively and authoritative experts should avoid one-sided
low. In the marginal topic, the waiting information propaganda or excessive propaganda and prevent
heat decreased throughout the progress of the misunderstandings and exaggerations. For example, on
pandemic. Moreover, the public’s attention to TCM the topic of integration of TCM and western medicine,
presented the following characteristics. (1) TCM is a academician Zhang Boli provided more pertinent
cultural image. In the later stage, China has made suggestions. While taking the lead in supporting the
remarkable achievements in fighting against TCM-based diagnosis and treatment, he also actively
COVID-19. After the outbreak of the COVID-19 in affirmed the role of Western medicine, guiding the
foreign countries, they were eager to obtain China’s public opinion to a more reasonable, objective and
prevention and control experience and material help. inclusive direction.
The characteristic prevention and control means Cultural confidence with national characteristics is
around TCM have raised the interest of other countries. gained by exploring the origin of TCM. The
(2) From individual case reports to the comparison of fundamental characteristic of TCM is not only its
international epidemic prevention measures, TCM has validation of the effectiveness, but also its unique
improved the cultural foundation. Due to the cultural foundation.
incomplete interventions in diagnosis and treatment, in A few limitations in our study were identified: (1)
the early stage, the reports of TCM-led treatments only data collection was challenging while representing the
caused small attention. However, TCM played an views of the whole network because of the biased
important role in the whole pandemic and, as a contrast differences of microblog users in terms of region, age,
between the Chinese national public impression and and occupation structure; (2) in the research
other countries; it has strengthened our cultural base. framework design of microblog hot search, there are
(3) Expert and media promoted TCM. In the late focus differences in participating users, so the comparability
of marginal topics, it can be found that the speeches of of multiple hot searches is low.
medical celebrities, experts, and media which
supported the promotion of TCM increased Conclusion
significantly. Opinion leaders and experts are the key
subjects to guide public opinion. In this study, the public opinion of TCM during the
COVID-19 pandemic was divided into different stages.
Discussion LSTM emotional analysis method and LDA topic
analysis method were used to study the change of
Government guidance is key in medical practice. In public emotional bias and the trend of public opinion
recent years, a number of policies to boost the topic in the heating up period, constant temperature
development of TCM have potentiated the period, and cooling period of TCM. The composition
development of TCM. During the COVID-19 of the public’s positive emotions before and after the
pandemic, the timely addition of TCM into the COVID-19 pandemic has improved significantly, and
diagnosis and treatment plan of the disease was an the public’s topics of concern changed from
opportunity given by the central government. government guidance to spontaneous praise by the
Social environment is essential for the development masses, from logistics supplementary prevention to
of the industry. TCM-based treatments, with a cure front-line main force, from pure drug orientation to
rate as high as 90%; further confirm that TCM is trust expert teams, from exaggerated publicity to
effective. Coupled with the media’s propaganda and objective communication, from domestic participation
the deeds of TCM workers, the mass support was be in fighting the pandemic to become an international
obtained in the later stage of the pandemic. example. The public opinion on using TCM against
To create a stable and positive public psychological COVID-19 improved throughout the timeline. This
environment, finding the scientific true is key. The trap study only focused on the changes and reasons of the
of “Tacitus” comes from inappropriate information overall public opinion characteristics of TCM during
propaganda and the construction of a stable and the pandemic. However, because of the inseparable
positive psychological environment. This information characteristics between TCM and traditional culture, it
should be seek steadily, not excessively praised or is challenging to maintain focus on the TCM benefits
denied, it should be based on facts and data, and firmly in a technological advanced modern society. The
Submit a manuscript: https://www.tmrjournals.com/hpm 11doi: 10.12032/HPM20210404031 REVIEW
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