Sentiment Impact of Public Health Agency communication Strategies on TikTok under COVID-19 Normalization: Deep Learning Exploration

Aim

The accessibility of social media data has allowed researchers to measure official-public interactions during COVID-19. However, previous work analyzing official posts or public comments has failed to explore the link between the two. Therefore, this study investigates the relationship between the communication strategies of public health agencies (PHAs) on TikTok and public emotional/sentiment tendencies in COVID-19 normalization.

Subject and methods

This study uses the 2022 Shanghai city closure event as a public health communication case study in the context of COVID-19 normalization, using TikTok as a data source. We first analyze the communication strategies adopted by the PHA based on the Crisis and Emergency Risk Communication (CERC) model. Then, we classify the sentiment of public comments using the Large-Scale Knowledge Enhanced Pre-Training for Language Understanding and Generation (ERNIE) pre-training model. Finally, we explore the connection between PHA communication strategies and public sentiment tendencies.

Results

First, the public's sentiment tendencies differ at different stages. Therefore, appropriate communication strategies should be developed stage-by-stage. Second, the public's emotional disposition to different communication strategies varies: government statements, vaccines, and prevention and control programs are more likely to produce a friendly comment environment, while policy and new cases per day are more likely to produce unfavorable comment content. However, this does not mean that policy and new cases per day should be avoided; the judicious use of these two strategies can help PHAs understand the current issues causing public dissatisfaction. Third, videos with celebrity appearances can significantly increase positive public sentiment and, thereby, public participation.

Conclusion

We propose an improved CERC guideline for China based on the Shanghai lockdown case.

Digital humanitarianism and crisis management: an empirical study of antecedents and consequences

Purpose

This study proposes a digital humanitarianism dynamic capability (DHDC) paradigm that explores the direct effects of DHDC on disaster risk reduction (DRR) and the mediating effects of process-oriented dynamic capabilities (PODC) on the relationship between DHDC and DRR.

Design/methodology/approach

To validate the proposed model, the authors used an offline survey to gather data from 260 district magistrates in India managing the COVID-19 pandemic.

Findings

The results affirm the importance of the DHDC system for DRR. The findings depict that the impact of PODC on DRR in the DHDC system is negligible. This study can help policymakers in planning during emergencies.

Research limitations/implications

Technological innovation has reshaped the way humanitarian organizations (HOs) respond to humanitarian crises. These organizations are able to provide immediate aid to affected communities through digital humanitarianism (DH), which involves significant innovations to match the specific needs of people in real-time through online platforms. Despite the growing need for DH, there is still limited know-how regarding how to leverage such technological concepts into disaster management. Moreover, the impact of DH on DRR is rarely examined.

Originality/value

The present study examines the impact of the dynamic capabilities of HOs on DRR by applying the resource-based view (RBV) and dynamic capability theory (DCT).

Impact of Social Media, Extended Parallel Process Model (EPPM) on the Intention to Stay at Home during the COVID-19 Pandemic

Considering how communities perceive the threat and risks of COVID-19, it is essential to examine how emotional regulation stimulated through intrinsic and extrinsic incentive mechanisms via social media can reinforce ‘Stay at home’ intentions. The conceptual framework was developed using the elements of the Extended Parallel Process Model (EPPM). A self-reported questionnaire was used to measure individuals’ intention to stay at home during the pandemic based on the perceived locus of causality as a part of self-determination theory. The empirical research was conducted on a sample of 306 USA respondents. The study results indicate that both components of the EPPM—efficacy and threat—positively affect ‘stay at home’ intentions. Moreover, a positive effect of efficacy on threats was found, as was a moderating effect of threats on the relationship between efficacy and the intention to stay at home. Meanwhile, the influence of social media exposure on threats and behavioral intentions was not significant. People are likely to stay at home as a preventive measure during COVID-19 if there is enough threats and efficacy. However, the abundance of information and opinions in social media can lead to a decreased perceived threat and might disrupt the acceptance of preventive actions.

Use of Social Media Data in Disaster Management: A Survey

Social media has played a significant role in disaster management, as it enables the general public to contribute to the monitoring of disasters by reporting incidents related to disaster events. However, the vast volume and wide variety of generated social media data create an obstacle in disaster management by limiting the availability of actionable information from social media. Several approaches have therefore been proposed in the literature to cope with the challenges of social media data for disaster management. To the best of our knowledge, there is no published literature on social media data management and analysis that identifies the research problems and provides a research taxonomy for the classification of the common research issues. In this paper, we provide a survey of how social media data contribute to disaster management and the methodologies for social media data management and analysis in disaster management. This survey includes the methodologies for social media data classification and event detection as well as spatial and temporal information extraction. Furthermore, a taxonomy of the research dimensions of social media data management and analysis for disaster management is also proposed, which is then applied to a survey of existing literature and to discuss the core advantages and disadvantages of the various methodologies.