Health Communication through Positive and Solidarity Messages Amid the COVID-19 Pandemic: Automated Content Analysis of Facebook Uses

Angela Chang, Xuechang Xian, Matthew Tingchi Liu and Xinshu Zhao
Additional contact information
Angela Chang: Department of Communication, Faculty of Social Sciences, University of Macau, Macao, China
Xuechang Xian: Department of Communication, Faculty of Social Sciences, University of Macau, Macao, China
Matthew Tingchi Liu: Department of Management and Marketing, Faculty of Business Administration, University of Macau, Macao, China
Xinshu Zhao: Department of Communication, Faculty of Social Sciences, University of Macau, Macao, China

IJERPH, 2022, vol. 19, issue 10, 1-16

Abstract: The COVID-19 outbreak has caused significant stress in our lives, which potentially increases frustration, fear, and resentful emotions. Managing stress is complex, but helps to alleviate negative psychological effects. In order to understand how the public coped with stress during the COVID-19 pandemic, we used Macao as a case study and collected 104,827 COVID-19 related posts from Facebook through data mining, from 1 January to 31 December 2020. Divominer, a big-data analysis tool supported by computational algorithm, was employed to identify themes and facilitate machine coding and analysis. A total of 60,875 positive messages were identified, with 24,790 covering positive psychological themes, such as “anti-epidemic”, “solidarity”, “hope”, “gratitude”, “optimism”, and “grit”. Messages that mentioned “anti-epidemic”, “solidarity”, and “hope” were the most prevalent, while different crisis stages, key themes and media elements had various impacts on public involvement. To the best of our knowledge, this is the first-ever study in the Chinese context that uses social media to clarify the awareness of solidarity. Positive messages are needed to empower social media users to shoulder their shared responsibility to tackle the crisis. The findings provide insights into users’ needs for improving their subjective well-being to mitigate the negative psychological impact of the pandemic.

Keywords: COVID-19; Facebook; positive psychology; solidarity; anti-epidemic; semantic analysis; natural language processing; automated content analysis (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/1660-4601/19/10/6159/pdf (application/pdf)
https://www.mdpi.com/1660-4601/19/10/6159/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jijerp:v:19:y:2022:i:10:p:6159-:d:818797

Access Statistics for this article

IJERPH is currently edited by Ms. Jenna Liu

More articles in IJERPH from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().