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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/).

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                                                          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.

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History & Philosophy of Medicine - TMR
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.

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   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)

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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.

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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

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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

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            Figure 9 Constant temperature period high proportion theme PyLDAvis module

                  Figure 10 Cooling period high proportion theme PyLDAvis module

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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

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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/hpm
History & 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                                                             11
doi: 10.12032/HPM20210404031                                                                    REVIEW
research methods of public opinion monitoring and          14. Jiang JB. Public opinion pays close attention to
analysis guidance can be used for TCM-based research           the promulgation of traditional Chinese medicine
strategies.                                                    law. China       News      Tradit   Chin   Med.
                                                               http://www.cntcm.com.cn/2017-01/05/content_25
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12                                                                Submit a manuscript: https://www.tmrjournals.com/hpm
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