Assessment of public attention, risk perception, emotional and behavioural responses to the COVID-19 outbreak: social media surveillance in China
By Z. Hou, Fanxing Du, Hao Jiang, Xiaoping Zhou, Leesa Lin. Social Science Research Network, 2020
124 citations recorded by the source corpus.
Using social media surveillance data, this study aimed to assess public attention, risk perception, emotion, and behavioural response to the COVID-19 outbreak in real time.We collected data from most popular social medias: Sina Weibo, Baidu search engine, and Ali e-commerce marketplace, from 1 Dec 2019 to 15 Feb 2020. Weibo post counts and Baidu searches were used to generate indices assessing public attention. Public intention and actual adoption of recommended protection measures or panic buying triggered by rumours and misinformation were measured by Baidu and Ali indices. Qualitative Weibo posts were analysed by the Linguistic Inquiry and Word Count text analysis programme to assess public emotion responses to epidemiological events, governments’ announcements, and control measures.We identified two missed windows of opportunity for early epidemic control of the COVID-19 outbreak, one in Dec 2019 and the other between 31 Dec and 19 Jan, when public attention was very low despite the emerging outbreak. Delayed release of information ignited negative public emotions. The public responded quickly to government announcements and adopted recommended behaviours according to issued guidelines. We found rumours and misinformation regarding remedies and cures led to panic buying during the outbreak, and timely clarification of rumours effectively reduced irrational behaviour.Social media surveillance can enable timely assessments of public reaction to risk communication and epidemic control measures, and the immediate clarification of rumours. This should be fully incorporated into epidemic preparedness and response systems.National Natural Science Foundation of China.
Subfields: Artificial Intelligence, Social Psychology, Sociology and Political Science
View Paper: https://www.semanticscholar.org/paper/72ed114ee0656b2c01aea985ac2939c14c415169
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
Citations in the corpus
- Artificial Intelligence (AI) and Big Data for Coronavirus (COVID-19) Pandemic: A Survey on the State-of-the-Arts (2020, background): “Similar to textual cues in [79], it is possible to collect spoken conversations from news, advertising videos, and social medias for further analyses.”
- Adoption of Digital Technologies in Health Care During the COVID-19 Pandemic: Systematic Review of Early Scientific Literature (2020, background): “…Clients/patients, health care providers Yan et al [20] Local Innovating Clients/patients, health systems/resource managers, data services Hou et al [21] Global Complementing Health care providers Feng et al [22] Global Complementing Health care providers Jin et al [23] Global Innovating…”