Influence of Strategic Crisis Communication on Public Perceptions during Public Health Crises: Insights from YouTube Chinese Media
Abstract
:1. Introduction
2. Research Background and Relevant Literature
2.1. The COVID-19 Pandemic
2.2. Relevant Literature
2.2.1. Situational Crisis Communication Theory (SCCT)
2.2.2. Evaluation of Crisis Communication Strategies
3. Data Processing and Research Methodology
3.1. Theoretical Concepts
- Media. In this study, “Media” refers to online media or video bloggers who disseminate news on video-sharing platforms. The professional quality criteria of media content is captured by its number of followers and years of operation; other characteristics of media content are measured by observable variables such as location (Mainland China, Hong Kong, Taiwan, or other regions), type (independent media or institutional media), and language (Traditional or Simplified Chinese), among others.
- The role of media in crisis communication. In the subsequent data collection and processing section, this study further narrows the scope of the media samples. Firstly, the study limits the samples to news and politics media, whose function is to disseminate and analyze news, thus shaping public perceptions. Secondly, the study restricts media samples to “Chinese media”, referring to Chinese channels/vloggers and pro-China channels/vloggers that primarily use the Chinese language on YouTube. While these constraints don’t guarantee that the media’s objectives during the COVID-19 pandemic were centered on crisis communication (although intuitively they might have been), they do ensure that the content created by these media contains a significant amount of crisis communication information, as shown by text analysis of their video titles. In other words, while the media may not have explicitly intended to engage in crisis communication, the videos they released, which may contain crisis communication content, effectively played a significant role in crisis communication. Therefore, by analyzing videos with crisis communication content and contrasting them with others, the effectiveness of specific crisis communication strategies can be assessed.
- The public. In this study, the public primarily includes the active Chinese-speaking audience on YouTube, as they represent the most closely related stakeholders overseas for China and are the primary target group for crisis communication. YouTube, as the world’s largest video-sharing platform, hosts a substantial body of video channels/bloggers sharing different types of content. This platform attracts a large audience who view videos and express personal opinions. As of January 2022, YouTube had 2.562 billion monthly active users worldwide [36]. Therefore, the target audience for crisis communication is not necessarily local, especially considering YouTube’s limited reach in mainland China.
3.2. Data Collection
- The approach for building the sampling frame of YouTube video channels/bloggers has been established by repeatedly searching for top keywords from top news in Mainland China and subsequently extracting video channel/blogger information from the search results. First, the top news keywords were extracted from major events in Mainland China for the time period spanning from 2019 to 2020 [38,39]. Correspondingly, examples of these keywords can be found in Appendix A Table A2. Second, video information corresponding to each keyword was retrieved on a daily basis from 31 December 2019, to 30 June 2020 through the application programming interface offered by YouTube API Services. Video channel/blogger information was obtained from these results. Since the YouTube API services yield results for up to 50 videos per keyword search, each keyword search for a specific day was iterated until no new video data emerged. By utilizing the aforementioned sampling method, the study sample includes the vast majority of Chinese-speaking YouTube video channels/bloggers who have posted videos associated with Mainland China between 2019 and 2020, even when not all Chinese-speaking vloggers are included in the sample. This ensures that the study sample is as close as possible to the entire population or at least meets the criteria of random sampling for Chinese-speaking video channels/bloggers on YouTube.
- The research sample is further narrowed down to channels/vloggers with a higher level of relevance to Mainland China. Specifically, through text analysis of video titles, video channels/bloggers who had 10% or more of their videos related to Mainland China topics were selected. Clearly, the study scope targets channels/vloggers closely linked to Mainland China topics due to the study’s focus on Chinese media’s crisis communication on YouTube during the COVID-19 pandemic. The stated strategy is meant to maximize the inclusion of potential crisis communication channels of China and its stakeholders overseas in this crisis event while minimizing the complexities in sample expansion. In the robustness analysis section, various samples were generated based on varying criteria for judgment.
- In alignment with the research objectives, channels/vloggers in the “News and Politics” category were selected. YouTube classifies video content into 15 primary categories, such as “News and Politics”, “Sports”, and “Autos and Vehicles”, among others. Notably, channels/vloggers in the “News and Politics” category served as the primary crisis communication channels for China on YouTube during the COVID-19 epidemic. Analyzing the videos they disseminated offers direct insight into the crisis communication strategies of Chinese media and their effectiveness.
- Channels/vloggers with fewer than 10,000 subscribers were excluded from the study sample. In fact, the content distribution mechanism of YouTube exhibits strong selectivity when boosting videos from smaller channels. As a consequence, the inclusion of smaller channels/vloggers could result in non-random sampling. The less influential channels/vloggers are filtered out by setting a minimum subscriber threshold, thereby mitigating potential non-random sampling concerns. In addition, the robustness analysis section presents the regression outcomes for samples with a minimum subscriber count of 20,000 and 50,000, respectively.
- Channels/vloggers with a discernible pro-China ideological stance were selected from the pool of candidate channels/vloggers. The ideological tendencies of these channels/vloggers were manually classified by human coders based on the ideologies conveyed in their content. Specifically, channels/vloggers that predominantly reported on China in a positive/neutral manner were categorized as either Chinese media or pro-China media. Throughout the COVID-19 pandemic, these channels/vloggers served as the main channels for China’s crisis communication on YouTube, forming the focal point of this research. Consequently, this research finalized the study sample of Chinese media.
- Video samples are derived from the study sample of Chinese media. In fact, the initial video on COVID-19 from YouTube Chinese media appeared on 31 December 2019. Following the lifting of the lockdown in Wuhan on April 8th, videos on this topic began to diminish. Therefore, in accordance with the progression of the COVID-19 epidemic, the authors selected videos that were published from 31 December 2019, to 31 May 2020, as video samples for this study. Additionally, since this study primarily focuses on crisis management strategies of Chinese media, the authors further narrowed down the extracted video samples to those pertinent to Mainland China topics in order to satisfy the parallel trend assumption integral to the DiD method, which is employed to evaluate the effectiveness of crisis communication strategies. Furthermore, in the robustness analysis section, the sample period is extended to 30 June 2020, while incorporating videos irrespective of their relevance to Mainland China topics.
- Finally, audience comment samples are obtained from the video dataset. In particular, all audience comments are gathered from the video dataset, with comments in “Simplified Chinese” or “Traditional Chinese” being filtered as the audience comment samples. Owing to standardization challenges in performing sentiment analysis across diverse languages or Emojis, the researchers excluded comments in non-Chinese languages and those containing only Emojis. As a result, a mere 3.23% of the comments were excluded, exerting negligible influence on the study conclusions due to their limited information.
3.3. Data Processing
3.3.1. Text Analysis
3.3.2. Sentiment Analysis
3.4. Research Methodology
3.5. Summary Statistics
4. Research Results
4.1. CCS Adopted by Chinese Media during the COVID-19 Pandemic
4.2. The Impact of Strategic Crisis Communication on Public Perceptions
4.3. Parallel Trends Test
4.4. Robustness Test
4.5. Heterogeneity Analysis
5. Discussion
6. Conclusions and Policy Implications
7. Limitations and Future Direction
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Appendix A
Strategies | Contents |
---|---|
Primary crisis response strategies | |
(1) Deny crisis response strategies | |
Attack the accuser | Crisis manager confronts the person or group claiming something is wrong with the organization. |
Denial | Crisis manager asserts that there is no crisis. |
Scapegoat | Crisis manager blames some person or group outside of the organization for the crisis. |
(2) Diminish crisis response strategies | |
Excuse | Crisis manager minimizes organizational responsibility by denying intent to do harm and/or claiming inability to control the events that triggered the crisis. |
Justification | Crisis manager minimizes the perceived damage caused by the crisis. |
(3) Rebuild crisis response strategies | |
Compensation | Crisis manager offers money or other gifts to victims. |
Apology | Crisis manager indicates the organization takes full responsibility for the crisis and asks stakeholders for forgiveness. |
Secondary crisis response strategies | |
(4) Bolstering crisis response strategies | |
Reminder | Tell stakeholders about the past good works of the organization. |
Ingratiation | Crisis manager praises stakeholders and/or reminds them of past good works by the organization. |
Victimage | Crisis managers remind stakeholders that the organization is a victim of the crisis too. |
Keywords | News Content |
---|---|
Chang’e 4 | The Chang’e 4 probe landed on the far side of the moon |
Fuxing | The Fuxing CR200J model train officially begins service in China |
Jiangsu chemical plant explosion | An explosion occurred at a chemical plant in Yancheng, Jiangsu, leading to at least 78 deaths |
Belt and Road | The Second Belt and Road Forum for International Cooperation was held from 25 to 27 April 2019 in Beijing |
Shanghai waste management | The “Regulations on the Management of Domestic Waste in Shanghai” came into effect, announcing that Shanghai had entered the era of mandatory household waste classification |
70th anniversary; military parade | China celebrates 70th anniversary with its biggest ever military parade |
Wuhan; Huanan Seafood Market; lockdown; COVID-19; Coronavirus; Zhong Nanshan; Huoshenshan Hospital; vaccines | The COVID-19 pandemic |
Peak carbon emissions; Carbon neutral | At the 2020 United Nations General Assembly, President Xi Jinping declared China’s objective to reach peak carbon emissions before 2030 and achieve carbon neutrality by 2060 |
China International Import Expo | The 3rd China International Import Expo was held in Shanghai |
Variables | Mean | S.D. | Observations |
---|---|---|---|
Video channels/bloggers | |||
Subscribers (thousand) | 294.67 | 363.22 | 104 |
Language (%) | |||
Simplified Chinese | 60.58 | 104 | |
Traditional Chinese | 39.42 | 104 | |
Media ownership (%) | |||
Institutional media | 45.19 | 104 | |
Independent media | 54.81 | 104 | |
Media location (%) | |||
Mainland China | 25.96 | 104 | |
Hong Kong | 18.27 | 104 | |
Taiwan | 22.12 | 104 | |
Others | 33.65 | 104 | |
Videos | |||
Number of Views | 33,789.28 | 110,313.91 | 13,253 |
Number of Likes | 879.21 | 2348.63 | 13,063 |
Number of Comments | 190.93 | 483.50 | 10,812 |
Video topics (%) | |||
COVID-19 related | 31.04 | 13,253 | |
Others | 68.97 | 13,253 | |
Comments | |||
Sentiment Index | 0.5354 | 0.0888 | 1,790,816 |
Positive comments (%) | 38.54 | 1,790,816 | |
Negative comments (%) | 32.41 | 1,790,816 |
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Mean | Observations | Mean | Observations | Mean Difference | |
---|---|---|---|---|---|
Positive Sentiment | Videos containing CCS (Treatment group) | Videos unrelated to CCS (Control groups) | |||
Stage Ⅰ | 0.28 | 1825 | 0.40 | 137,758 | −0.12 *** |
Stage II | 0.38 | 524,247 | 0.39 | 1,126,986 | −0.01 *** |
Sentiment Index | Videos containing CCS (Treatment group) | Videos unrelated to CCS (Control groups) | Mean Difference | ||
Stage Ⅰ | 0.46 | 1825 | 0.55 | 137,758 | −0.09 *** |
Stage II | 0.53 | 524,247 | 0.54 | 1,126,986 | −0.01 *** |
Deny | Diminish | Rebuild | Enhancing | Observations | ||
---|---|---|---|---|---|---|
Stage I | Stage 1 | 23.91 | 28.26 | 0 | 69.57 | 46 |
Stage II | Stage 2 | 14.57 | 18.65 | 0.33 | 82.89 | 1496 |
Stage 3 | 11.94 | 23.75 | 0.70 | 86.56 | 863 | |
Stage 4 | 11.23 | 33.77 | 1.30 | 87.49 | 1158 | |
Stage 5 | 15.64 | 37.64 | 0.73 | 86 | 550 | |
Total | 13.32 | 26.62 | 0.73 | 85.22 | 4113 |
Variables | (a) Positive Sentiment | (b) Sentiment Index |
---|---|---|
CCS × Stage II | 0.0410 *** | 0.0287 *** |
(0.0142) | (0.0084) | |
Control variables | Yes | Yes |
Fixed effect | Yes | Yes |
Observations | 1,790,816 | 1,790,816 |
Variables | (a) Positive Sentiment | (b) Sentiment Index |
---|---|---|
(a) Bloggers whose videos contained 5% or more of content related to mainland China | ||
CCS × Stage II | 0.0413 *** | 0.0291 *** |
Observations | (0.0142) | (0.0084) |
Observations | 1,802,300 | 1,802,300 |
(b) Bloggers whose videos contained 15% or more of content related to mainland China | ||
CCS × Stage II | 0.0374 ** | 0.0244 *** |
(0.0143) | (0.0079) | |
Observations | 1,737,070 | 1,737,070 |
(c) Samples with subscription counts greater than or equal to 20,000 | ||
CCS × Stage II | 0.0412 *** | 0.0288 *** |
(0.0142) | (0.0084) | |
Observations | 1,783,276 | 1,783,276 |
(d) Samples with subscription counts greater than or equal to 50,000 | ||
CCS × Stage II | 0.0415 *** | 0.0287 *** |
(0.0143) | (0.0085) | |
Observations | 1,711,633 | 1,711,633 |
(e) Samples from 31 December 2019 to 30 June 2020 | ||
CCS × Stage II | 0.0409 *** | 0.0295 *** |
(0.0133) | (0.0093) | |
Observations | 2,235,291 | 2,235,291 |
(f) Samples (Including topics unrelated to mainland China) from 31 December 2019 to 31 May 2020 | ||
CCS × Stage II | 0.0493 *** | 0.0366 *** |
(0.0157) | (0.0130) | |
Observations | 3,613,356 | 3,613,356 |
(g) Probit regression | ||
CCS × Stage II | 0.1178 *** | 0.1095 *** |
(0.0402) | (0.0263) | |
Observations | 1,790,816 | 1,790,816 |
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Sun, D.; Li, Y. Influence of Strategic Crisis Communication on Public Perceptions during Public Health Crises: Insights from YouTube Chinese Media. Behav. Sci. 2024, 14, 91. https://doi.org/10.3390/bs14020091
Sun D, Li Y. Influence of Strategic Crisis Communication on Public Perceptions during Public Health Crises: Insights from YouTube Chinese Media. Behavioral Sciences. 2024; 14(2):91. https://doi.org/10.3390/bs14020091
Chicago/Turabian StyleSun, Dan, and Yiping Li. 2024. "Influence of Strategic Crisis Communication on Public Perceptions during Public Health Crises: Insights from YouTube Chinese Media" Behavioral Sciences 14, no. 2: 91. https://doi.org/10.3390/bs14020091