Unpacking the black box: How to promote citizen engagement through government social media during the COVID-19 crisis

Abstract

A R T I C L E I N F OKeywords: government social media citizen engagement dialogic communication theory media richness theory emotional valence crisis management A B S T R A C TDuring times of public crises, governments must act swiftly to communicate crisis information effectively and efficiently to members of the public; failure to do so will inevitably lead citizens to become fearful, uncertain and anxious in the prevailing conditions. This pioneering study systematically investigates how Chinese central government agencies used social media to promote citizen engagement during the COVID-19 crisis. Using data scraped from 'Healthy China', an official Sina Weibo account of the National Health Commission of China, we examine how citizen engagement relates to a series of theoretically relevant factors, including media richness, dialogic loop, content type and emotional valence. Results show that media richness negatively predicts citizen engagement through government social media, but dialogic loop facilitates engagement. Information relating to the latest news about the crisis and the government's handling of the event positively affects citizen engagement through government social media. Importantly, all relationships were contingent upon the emotional valence of each Weibo post. )is researcher at the School of Journalism and New Media, Xi'a Jiaotong University in China. She has been studying for information behavior and interaction, and information analysis on the new media.Richard Evans (richard.evans@brunel.ac.uk) is a Senior Lecturer at the College of Engineering, Design and Physical Sciences, Brunel University London, United Kingdom. His research interests are social media and organizational behavior, and knowledge management.

Resilience against crises: COVID-19 and lessons from natural disasters

“…Previous studies show that technology is central to making sense of crises (e.g., Madon, 2005;Payton & Kiwanuka-Tondo, 2009). Indeed, the ongoing inquiry into understanding COVID-19 suggests that the effective management of such pandemics relies heavily on digital and data-driven solutions and resilient systems (e.g., Chen et al, 2020;Djalante et al, 2020;Herrera et al, 2020;Nembhard et al, 2020;Ting et al, 2020).…”

Mental health among COVID-19 survivors and healthcare workers exposed to COVID-19 in Wuhan, China: a cross-sectional study

“…Patients were forced isolation in speci c hospitals, which divided them from work, family, and conventional daily life, so that to plumb the depths of loneliness, humiliation, and even despair 32 . Besides, some bad media reported false news to mislead public which aggravated patients' panic and anxiety 33 . Results analysis showed that it was important to construct an appropriate emergency response mechanism of serious infectious disease and recovery system of post-traumatic stress.…”

Track Iran's national COVID-19 response committee’s major concerns using two-stage unsupervised topic modeling

“…Lazard et al [ 35 ] and Kim et al [ 52 ] utilized the LDA-based public concern tracking in the 2014-2015 season Ebola outbreak, and Glowacki et al [ 53 ] analyzed the CDC’s communications in response to the public concerns after the spreading of Zika in Florida. The rapid rate of COVID-19 spread alerts the global community to respond promptly to medical and mental concerns to accelerate the action of disease control [ 39 ]. In this way, Dong et al [ 54 ], Stokes et al [ 55 ], and Liu et al [ 40 ] employed topic modeling techniques to detect public concerns and health communications.…”

“…This dataset can be used in association with a policy timeline to reveal the public reactions to precaution, measures set up, and acts. Chen et al [ 39 ] investigated the Citizenship Engagement through Government Social Media (CEGSM) in China’s earlier post-COVID-19 period. Their results suggest that parameters, such as media richness (vs. plain text), the length of the in-media response time, dialogic loop, the topic diversity of the announced news posts, and the polarity of the government response posts (Pos., Neg., and Neut.)…”