Chinese Public's Attention to the COVID-19 Epidemic on Social Media: Observational Descriptive Study

Background Since the coronavirus disease (COVID-19) epidemic in China in December 2019, information and discussions about COVID-19 have spread rapidly on the internet and have quickly become the focus of worldwide attention, especially on social media. Objective This study aims to investigate and analyze the public’s attention to events related to COVID-19 in China at the beginning of the COVID-19 epidemic (December 31, 2019, to February 20, 2020) through the Sina Microblog hot search list. Methods We collected topics related to the COVID-19 epidemic on the Sina Microblog hot search list from December 31, 2019, to February 20, 2020, and described the trend of public attention on COVID-19 epidemic-related topics. ROST Content Mining System version 6.0 was used to analyze the collected text for word segmentation, word frequency, and sentiment analysis. We further described the hot topic keywords and sentiment trends of public attention. We used VOSviewer to implement a visual cluster analysis of hot keywords and build a social network of public opinion content. Results The study has four main findings. First, we analyzed the changing trend of the public’s attention to the COVID-19 epidemic, which can be divided into three stages. Second, the hot topic keywords of public attention at each stage were slightly different. Third, the emotional tendency of the public toward the COVID-19 epidemic-related hot topics changed from negative to neutral, with negative emotions weakening and positive emotions increasing as a whole. Fourth, we divided the COVID-19 topics with the most public concern into five categories: the situation of the new cases of COVID-19 and its impact, frontline reporting of the epidemic and the measures of prevention and control, expert interpretation and discussion on the source of infection, medical services on the frontline of the epidemic, and focus on the worldwide epidemic and the search for suspected cases. Conclusions Our study found that social media (eg, Sina Microblog) can be used to measure public attention toward public health emergencies. During the epidemic of the novel coronavirus, a large amount of information about the COVID-19 epidemic was disseminated on Sina Microblog and received widespread public attention. We have learned about the hotspots of public concern regarding the COVID-19 epidemic. These findings can help the government and health departments better communicate with the public on health and translate public health needs into practice to create targeted measures to prevent and control the spread of COVID-19.

[1]  C. Viboud,et al.  Early epidemiological analysis of the coronavirus disease 2019 outbreak based on crowdsourced data: a population-level observational study , 2020, The Lancet Digital Health.

[2]  J. Zarocostas How to fight an infodemic , 2020, The Lancet.

[3]  Yuanli Liu,et al.  Public Perception on Healthcare Services: Evidence from Social Media Platforms in China , 2019, International journal of environmental research and public health.

[4]  Zion Tsz Ho Tse,et al.  Social Media's Initial Reaction to Information and Misinformation on Ebola, August 2014: Facts and Rumors , 2016, Public health reports.

[5]  Nanping Wu,et al.  Correlation between reported human infection with avian influenza A H7N9 virus and cyber user awareness: what can we learn from digital epidemiology? , 2014, International journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases.

[6]  Sumiko Mekaru,et al.  Open access epidemiological data from the COVID-19 outbreak , 2020, The Lancet Infectious Diseases.

[7]  Zion Tsz Ho Tse,et al.  Ebola virus disease and social media: A systematic review. , 2016, American journal of infection control.

[8]  T. Bossomaier,et al.  Quantifying Network Dynamics and Information Flow Across Chinese Social Media During the African Ebola Outbreak , 2017, Disaster Medicine and Public Health Preparedness.

[9]  Ruchong Chen,et al.  Cancer patients in SARS-CoV-2 infection: a nationwide analysis in China , 2020, The Lancet Oncology.

[10]  Gary B. Wilcox,et al.  Public reactions to e-cigarette regulations on Twitter: a text mining analysis , 2017, Tobacco Control.

[11]  M. Lipsitch,et al.  Defining the Epidemiology of Covid-19 - Studies Needed. , 2020, The New England journal of medicine.

[12]  Yicheng Fang,et al.  CT Manifestations of Two Cases of 2019 Novel Coronavirus (2019-nCoV) Pneumonia , 2020, Radiology.

[13]  Toomas Timpka,et al.  Importance of Internet Surveillance in Public Health Emergency Control and Prevention: Evidence From a Digital Epidemiologic Study During Avian Influenza A H7N9 Outbreaks , 2014, Journal of medical Internet research.

[14]  Jinzhao Song,et al.  A Single and Two-Stage, Closed-Tube, Molecular Test for the 2019 Novel Coronavirus (COVID-19) at Home, Clinic, and Points of Entry , 2020, ChemRxiv : the preprint server for chemistry.

[15]  Z. Wang,et al.  A simple laboratory parameter facilitates early identification of COVID-19 patients , 2020, medRxiv.

[16]  Philip M. Massey,et al.  Applying Multiple Data Collection Tools to Quantify Human Papillomavirus Vaccine Communication on Twitter , 2016, Journal of medical Internet research.

[17]  Wenbiao Hu,et al.  Avian Influenza A (H7N9) and related Internet search query data in China , 2019, Scientific Reports.

[18]  S. Zhang,et al.  Clinical findings in a group of patients infected with the 2019 novel coronavirus (SARS-Cov-2) outside of Wuhan, China: retrospective case series , 2020, BMJ.

[19]  Jing Liu,et al.  Asymptomatic cases in a family cluster with SARS-CoV-2 infection , 2020, The Lancet Infectious Diseases.

[20]  D A Asch,et al.  The content of social media's shared images about Ebola: a retrospective study. , 2015, Public health.

[21]  Yi Hao,et al.  Chinese social media reaction to the MERS-CoV and avian influenza A(H7N9) outbreaks , 2013, Infectious Diseases of Poverty.

[22]  Ziding Zhang,et al.  Isolation and Characterization of 2019-nCoV-like Coronavirus from Malayan Pangolins , 2020, bioRxiv.

[23]  Biyao Liu,et al.  Assessing cyber-user awareness of an emerging infectious disease: evidence from human infections with avian influenza A H7N9 in Zhejiang, China. , 2015, International journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases.

[24]  Kwok-Hung Chan,et al.  Consistent Detection of 2019 Novel Coronavirus in Saliva , 2020, Clinical infectious diseases : an official publication of the Infectious Diseases Society of America.

[25]  Jenine K. Harris,et al.  Geospatial Distribution of Local Health Department Tweets and Online Searches About Ebola During the 2014 Ebola Outbreak , 2017, Disaster Medicine and Public Health Preparedness.

[26]  Jeremy C. Smith,et al.  Repurposing Therapeutics for COVID-19: Supercomputer-Based Docking to the SARS-CoV-2 Viral Spike Protein and Viral Spike Protein-Human ACE2 Interface , 2020 .

[27]  Linda Shields,et al.  Content Analysis , 2015 .

[28]  Potential inhibitors against papain-like protease of novel coronavirus (SARS-CoV-2) from FDA approved drugs , 2020 .

[29]  Lei Liu,et al.  Evaluating the accuracy of different respiratory specimens in the laboratory diagnosis and monitoring the viral shedding of 2019-nCoV infections , 2020, medRxiv.

[30]  Artem Cherkasov,et al.  Rapid Identification of Potential Inhibitors of SARS‐CoV‐2 Main Protease by Deep Docking of 1.3 Billion Compounds , 2020, Molecular informatics.

[31]  G. Chowell,et al.  Early epidemiological assessment of the transmission potential and virulence of coronavirus disease 2019 (COVID-19) in Wuhan City, China, January–February, 2020 , 2020, BMC Medicine.

[32]  R. Merchant,et al.  Public sentiment and discourse about Zika virus on Instagram. , 2017, Public health.

[33]  Zion Tsz Ho Tse,et al.  How people react to Zika virus outbreaks on Twitter? A computational content analysis. , 2016, American journal of infection control.

[34]  W. Silverstein,et al.  First imported case of 2019 novel coronavirus in Canada, presenting as mild pneumonia , 2020, The Lancet.

[35]  N. Ajami,et al.  Evidence of recombination in coronaviruses implicating pangolin origins of nCoV-2019 , 2020, bioRxiv.

[36]  Mowafa Said Househ,et al.  Communicating Ebola through social media and electronic news media outlets: A cross-sectional study , 2016, Health Informatics J..

[37]  Jieliang Chen,et al.  Pathogenicity and transmissibility of 2019-nCoV—A quick overview and comparison with other emerging viruses , 2020, Microbes and Infection.

[38]  Huachen Zhu,et al.  Identification of 2019-nCoV related coronaviruses in Malayan pangolins in southern China , 2020, bioRxiv.

[39]  C. Zheng,et al.  Time Course of Lung Changes at Chest CT during Recovery from Coronavirus Disease 2019 (COVID-19) , 2020 .

[40]  X. Wan,et al.  Are pangolins the intermediate host of the 2019 novel coronavirus (2019-nCoV) ? , 2020, bioRxiv.