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Title:	COVID危機溝通-研析網路輿情對滾動式防疫訊息之反應與影響 A Study on Crisis Communication of COVID – The Interaction of Internet Public Opinion and Sentiment with Agile Policy Messages
Authors:	詹子潁 Chan, Zi-Ying
Contributors:	蕭乃沂 Hsiao, Nai-Yi 詹子潁 Chan, Zi-Ying
Keywords:	危機溝通網路輿情情境危機傳播理論框架情緒分析 Crisis Communication Online Public Opinion Situational Crisis Communication Theory Framing Sentiment Analysis
Date:	2024
Issue Date:	2025-02-04 15:57:03 (UTC+8)
Abstract:	當COVID-19疫情衝擊全球，各國政府必須在不充足資訊與有限時間，因應疫情快速發展動態調整防疫措施，而頻繁變更政策訊息(shifting messages)往往引發輿論爭議和質疑。本研究主要目的，旨在於探討台灣防疫期間中央疫情指揮中心(CECC)如何藉SCCT危機溝通策略應對輿論壓力、並同步分析社群網路輿情對於滾動式防疫訊息的情緒與反應、輿情框架，以及對政策制定之影響。研究採用多重個案研究法與內容分析，透過 OpView 社群口碑資料庫蒐集輿情大數據，以「入境普篩」和「自費隔離」兩爭議個案為研究對象。實證結果發現台灣疫情期間網路輿情以「負面」情緒為主，P/N值長時間低於1，尤在政策轉向、缺乏充分溝通與一致標準時，負面情緒與聲量顯著升高。其次，危機溝通策略有效性視證據與控制能力多寡、利害關係人判別歸因責任高低而定。個案以「重建」策略搭配行動效果最佳，「否認」和「減責」等則對於降低負面傷害成效有限。最後，台灣防疫輿情框架呈現由多元向「對立」集中與「嘲諷」增長的趨勢，反映出網路意見二元對立、極化不滿的傾向，或與「政治」相關聯，框架於社會建構過程競爭主流敘事地位。個案分析發現，網路輿情正成為一種新興去中心化政治溝通工具，擴大公共參與的範圍。研究建議政府與組織應於重點決策前，建立主題式輿情監測機制，透過社群聆聽了解輿情變化列入參考;在危機溝通過程，以「接納責任」取代「防衛抗辯」策略具體回應大眾訴求。未來相關研究可進一步探討AI技術在進階輿情分析的應用，以及不同社群平台的輿情特徵與影響力。 The global COVID-19 pandemic forced governments to respond to rapidly evolving crises with limited information and constrained time. The frequent adjustments in public health measures and shifting policy messages often triggered public controversy and skepticism. This study aims to explore how Taiwan's Central Epidemic Command Center (CECC) employed crisis communication strategies of SCCT to address public pressure during the pandemic. Additionally, it analyzes the emotional reactions and responses, framing within social networks to dynamic pandemic messaging and assesses the impact of these reactions on policy-making processes. Using a multiple-case study approach and content analysis, the research leveraged big data from the OpView social listening and AI-powered public opinion monitoring platform. Two contentious cases, " General Screening" and "Self-paid Quarantine," were selected for analysis. Findings revealed that online public sentiment in Taiwan during the pandemic was predominantly negative, with P/N ratios consistently below 1. Negative emotions and sentiment surged, particularly during instances of policy shifts, inadequate communication, or inconsistent standards. The effectiveness of crisis communication strategies depended on the sufficiency of evidence, the level of control over the situation, and stakeholders' attributions of responsibility. Among the strategies employed, the combination of "Rebuilding" with "action-oriented" responses proved most effective, while strategies such as "Denial" and "Diminishment" had limited success in mitigating reputational damage. The study also identified a growing trend of shifting framing from diversity toward “adversary” and “sarcasm” in public sentiment, reflecting the binary divisions, polarization and dissatisfaction prevalent within online communities. These dynamics were closely linked to political affiliations and represented a competitive process of framing dominant narratives in social construction. Case analysis further highlighted that online public opinion has become an emergent decentralized political communication tool, enhancing public engagement and influencing policy-making. This study recommends that governments and organizations establish thematic public opinion monitoring mechanisms prior to key policy decisions, utilizing social listening to understand sentiment trends and inform decision-making processes. In crisis communication, adopting strategies of " accommodation" rather than " defensive," coupled with concrete action plans, is critical for addressing public concerns. Future research should investigate the application of AI technologies for advanced sentiment analysis and examine the distinct characteristics and influence of various social media platforms.
Reference:	中文文獻王慧蘭(2021)。台灣新冠肺炎網路新聞報導之大數據分析—以某群聚感染事件為例。國立臺灣大學公共衛生碩士論文，未出版，台北。友野典男(2023)。有限理性：行為經濟學入門首選！經濟學和心理學的共舞，理解人類真實行為的最佳工具。謝敏怡(譯)，新北:大牌出版。譯自: 行動経済学経済は「感情」で動いている. 2006. 古斯塔夫．勒龐 (2020)。烏合之眾：激情、非理性、領袖崇拜，盲目群體的心理陷阱。董強(譯)，台北: 時報出版。譯自: Psychologie Des Foules, Gustave Le Bon. 1985. 袁千雅(2021)。新冠肺炎防疫政策說服效果：公衛部門臉書個人化敘事對政策支持度與防疫意圖之影響。國立交通大學客家學院傳播與科技學系碩士論文，未出版，新竹。許悅(2021)。WHO與台灣CDC在新冠肺炎公關稿之主題框架及風險訊息差異之研究。國立政治大學傳播碩士學位論文，未出版，台北。張巧函(2023)。從經濟人走向社會人: 對公共政策理論的再詮釋。公共行政學報，(66)，117-137。張昱維、蔡易昌、楊惠春、樊聖(2021)。Google Trends 搜尋關鍵字熱度與 COVID-19 疫情趨勢的相關性-以臺灣為例的網路行為觀察性研究。醫學與健康期刊，10 (3)，17-31。張寶仁(2021)。從臺灣口罩政策探討新冠疫情之政府危機治理能力。國立中興大學國家政策與公共事務研究所碩士論文，未出版，台中。麥克魯漢(2006)。認識媒體 : 人的延伸。鄭明萱譯，台北:貓頭鷹出版。譯自Understanding Media: The Extensions of Man, Marshall McLuhan. 1964. 康納曼(2023)。快思慢想。洪蘭譯，台北: 天下文化出版。譯自Thinking, Fast and Slow. Kahneman, D. 2013. 曾偉恩 (2022)。COVID-19 疫苗施打與網路輿情聲量關係: 以 Moderna 疫苗為例。國立政治大學經濟學系碩士論文，未出版，台北。黃懿慧. (2016)。危機溝通策略與危機回應形式: 危機類型為權變因素之效果研究。傳播與社會學刊，(38)，135-164. 楊士逸 (2022)。網路輿情聲量對高端疫苗施打量的影響。國立政治大學經濟學系碩士論文，未出版，台北。鐘千惠(2022)。以文字探勘分析COVID-19疫苗採購政策溝通。國立中興大學國家政策與公共事務碩士論文，未出版，台中。鐘育騏(2022)。報紙社論對臺灣新冠肺炎疫情之文本分析：以《自由時報》與《中國時報》為例。世新大學口語傳播暨社群媒體學系碩士論文，未出版，台北。梁如馨(2021)。影響政府危機溝通良窳因子之研究：以我國防治新型冠狀病毒（COVID-19）與太魯閣號事故為例。國立臺灣大學政治學系碩士論文，未出版，台北。梁淑媛, 莊宇慧, 吳淑芳(2012)。內容分析技巧在護理質性資料之初步應用。護理雜誌，59(5), 84-90. 臧國仁 (1998)。消息來源組織與媒介真實之建構—組織文化與組織框架的觀點。廣告學研究，11: 69-116。英文文獻 Austin, L., Liu, B. F., & Jin, Y. (2011). How publics respond to crisis communication strategies: The interplay of information form and source. Public Relations Review, 37(4), 345-353. Austin, L., Liu, B. F., & Jin, Y. (2012). How audiences seek out crisis information: Exploring the social-mediated crisis communication model. Journal of Applied Communication Research, 40(2), 188-207. Berelson, B. (1952). Content Analysis In Communication Research. Glencoe, Illinois: The Free Press. Bessi, A. (2016). Personality traits and echo chambers on Facebook. Computers in Human Behavior, 65, 319-324. Bodas, M., & Peleg, K. (2020). Income assurances are a crucial factor in determining public compliance with self-isolation regulations during the COVID-19 outbreak–cohort study in Israel. Israel Journal of Health Policy Research, 9(1), 1-10. Boin, A. (2019). The transboundary crisis: Why we are unprepared and the road ahead. Journal of Contingencies and Crisis Management, 27(1), 94-99. Bradford, J. L., & Garrett, D. E. (1995). The effectiveness of corporate communicative responses to accusations of unethical behavior. Journal of Business Ethics, 14, 875-892. Capano, G., Howlett, M., Jarvis, D. S., Ramesh, M., & Goyal, N. (2020). Mobilizing policy (in) capacity to fight COVID-19: Understanding variations in state responses. Policy and Society, 39(3), 285-308. Chandra, R., & Krishna, A. (2021). COVID-19 sentiment analysis via deep learning during the rise of novel cases. PloS one, 16(8), e0255615. Coombs, W. T. (2004). Impact of past crises on current crisis communication: Insights from situational crisis communication theory. The Journal of Business Communication (1973), 41(3), 265-289. Coombs, W. T., & Huang, Y.-H. C. (2020). Revisiting the situational crisis communication theory. Communication & Society, 53, 1–19 Coombs, W. T., & Holladay, S. J. (2001). An extended examination of the crisis situations: A fusion of the relational management and symbolic approaches. Journal of Public Relations Research, 13(4), 321-340. Coombs, W. T. (2007). Protecting organization reputations during a crisis: The development and application of situational crisis communication theory. Corporate Reputation Review, 10, 163- 176. Coombs, W. T., & Holladay, S. J. (2007). The negative communication dynamic: Exploring the impact of stakeholder affect on behavioral intentions. Journal of Communication Management, 11(4), 300-312. Coombs, W. T., & Holladay, S. J. (2014). How publics react to crisis communication efforts: Comparing crisis response reactions across sub-arenas. Journal of Communication Management, 18(1), 40-57. Dagnall, N., Drinkwater, K. G., Denovan, A., & Walsh, R. S. (2020). Bridging the gap between UK government strategic narratives and public opinion/behavior: Lessons from COVID-19. Frontiers in Communication, 5, 71. De Matteis, A., Turkmen Ceylan, F. B., Adrianopoli, M., & Ertek, S. (2021). The contribution of testing in the fight against COVID-19: evidence from Italy. European Journal of Public Health, 31(4), 913-917. Diderichsen, F. (2021). How did Sweden fail the pandemic?. International Journal of Health Services, 51(4), 417-422. Drescher, L. S., Roosen, J., Aue, K., Dressel, K., Schär, W., & Götz, A. (2021). The spread of COVID-19 crisis communication by German public authorities and experts on Twitter: quantitative content analysis. JMIR Public Health and Surveillance, 7(12), e31834. Dutton, W. H. (2009). The fifth estate emerging through the network of networks. Prometheus, 27(1), 1-15. Entman, R. M. (1993). Framing: Toward clarification of a fractured paradigm. Journal of Communication, 43(4), 51-58. Gamson, W. A. (1995). Constructing social protest. Johnston H., Klandermans B. (eds) Social Movements and Culture. pp. 85-106. Gamson, W. A., & Wolfsfeld, G. (1993). Movements and media as interacting systems. The Annals of the American Academy of Political and Social Science, 528(1), 114-125. Gerbner, G. (1969). Toward" cultural indicators": The analysis of mass mediated public message systems. AV Communication Review, 137-148. Gitlin, T. (1980) The Whole World Is Watching: Mass Media in the Making and Unmaking of the New Left. University of California Press, Berkeley. Goffman, E. (1974). Frame Analysis: An Essay on the Organization of Experience. Harvard University Press. Hargadon, A. B., & Douglas, Y. (2001). When innovations meet institutions: Edison and the design of the electric light. Administrative Science Quarterly, 46(3), 476-501. Hussain, A., Tahir, A., Hussain, Z., Sheikh, Z., Gogate, M., Dashtipour, K., Ali, A.,& Sheikh, A. (2021) Artificial intelligence–enabled analysis of public attitudes on facebook and twitter toward covid-19 vaccines in the united kingdom and the united states: Observational study. Journal of Medical Internet Research 23(4), e26627. Favero, N., & Pedersen, M. J. (2020). How to encourage ‘togetherness by keeping apart’ amid COVID-19? The ineffectiveness of prosocial and empathy appeals. Journal of Behavioral Public Administration, 3(2), 1–19. Fredriksson, M., & Hallberg, A. (2021). COVID-19 testing in Sweden during 2020–split responsibilities and multi-level challenges. Frontiers in Public Health, 9, 754861. Jo, W., & Chang, D. (2020). Political consequences of COVID-19 and media framing in South Korea. Frontiers in Public Health, 8, 425. Hall, P. A. (1993). Policy paradigms, social learning, and the state: the case of economic policymaking in Britain. Comparative Politics, 275-296. Hallahan, K. (1999). Seven models of framing: Implications for public relations. Journal of Public Relations Research, 11(3), 205-242. Hameleers, M. (2020). Prospect theory in times of a pandemic: The effects of gain versus loss framing on policy preferences and emotional responses during the 2020 coronavirus outbreak–Evidence from the US and the Netherlands. Mass Communication and Society, 24(4), 479–499. Heffner, J., Vives, M. L., & FeldmanHall, O. (2021). Emotional responses to prosocial messages increase willingness to self-isolate during the COVID-19 pandemic. Personality and Individual Differences, 170, 110420. Hung, S. C., & Chang, S. C. (2023). Framing the virus: The political, economic, biomedical and social understandings of the COVID-19 in Taiwan. Technological Forecasting and Social Change, 188, 122276. Kalokairinou, L., Zettler, P. J., Nagappan, A., Kyweluk, M. A., & Wexler, A. (2020). The promise of direct-to-consumer COVID-19 testing: ethical and regulatory issues. Journal of Law and the Biosciences, 7(1), lsaa069. Kassarjian, H. H. (1977). Content analysis in consumer research. The Journal of Consumer Research, 4(1), 8-18. Kwok, L., Lee, J., & Han, S. H. (2022). Crisis communication on social media: what types of COVID-19 messages get the attention? Cornell Hospitality Quarterly, 63(4), 528-543. Kwok, S. W. H., Vadde, S. K., & Wang, G. (2021). Twitter speaks: an analysis of Australian Twitter users’ topics and sentiments about COVID-19 vaccination using machine learning. J Med Internet Res, 23(5), e26953. Leiss, W. (1996). Three phases in the evolution of risk communication practice. The Annals of the American Academy of Political and Social Science, 545(1), 85-94. Lundgren, R. E., & McMakin, A. H. (2018). Risk communication: A Handbook for Communicating Environmental, Safety, and Health Risks. John Wiley & Sons. McAuley, E., Duncan, T. E., & Russell, D. W. (1992). Measuring causal attributions: The revised causal dimension scale (CDSII). Personality and Social Psychology Bulletin, 18(5), 566-573. Melton, C. A., Olusanya, O. A., Ammar, N., & Shaban-Nejad, A. (2021). Public sentiment analysis and topic modeling regarding COVID-19 vaccines on the Reddit social media platform: A call to action for strengthening vaccine confidence. Journal of Infection and Public Health, 14(10), 1505-1512. Newton, K. (2020). Government communications, political trust and compliant social behaviour: The politics of Covid-19 in Britain. The Political Quarterly, 91(3), 502–513. Nielsen, J. H., & Lindvall, J. (2021). Trust in government in Sweden and Denmark during the COVID-19 epidemic. West European Politics, 44(5-6), 1180-1204. Park, Y. E. (2022). Developing a COVID-19 crisis management strategy using news media and social media in big data analytics. Social Science Computer Review, 40(6), 1358-1375. Reshi, A. A., et al. "Covid-19 vaccination-related sentiments analysis: A case study using worldwide twitter dataset." Healthcare. Vol. 10. No. 3. MDPI, 2022. Petridou, E., & Zahariadis, N. (2021). Staying at home or going out? Leadership response to the COVID-19 crisis in Greece and Sweden. Journal of Contingencies and Crisis Management, 29, 293–302. Rickard, L. N. (2021). Pragmatic and (or) constitutive? On the foundations of contemporary risk communication research. Risk analysis, 41(3), 466-479. Piedrahita-Valdés, H.,et al. (2021). Vaccine hesitancy on social media: Sentiment analysis from June 2011 to April 2019. Vaccines, 9(1), 28. Roberts, M. E., Stewart, B. M., & Tingley, D. (2019). Stm: An R package for structural topic models. Journal of Statistical Software, 91, 1-40. Teddlie, C., & Yu, F. (2007). Mixed methods sampling: A typology with examples. Journal of Mixed Methods Research, 1(1), 77-100. Tsao, S. F., MacLean, A., Chen, H., Li, L., Yang, Y., & Butt, Z. A. (2022). Public attitudes during the second lockdown: sentiment and topic analyses using tweets from Ontario, Canada.International Journal of Public Health, 67, 1604658. Tuchman, G. (1987). Representation and the news narrative: the web of facticity. American Media and Mass Culture: Left Perspectives, 331-44. Seale, H., Dyer, C. E., Abdi, I., Rahman, K. M., Sun, Y., Qureshi, M. O., Dowell-Day, A., Sward,J., & Islam, M. S. (2020). Improving the impact of non-pharmaceutical interventions during COVID-19: examining the factors that influence engagement and the impact on individuals. BMC Infectious Diseases, 20, 1-3. Shih, T. J., Wijaya, R., & Brossard, D. (2008). Media coverage of public health epidemics: Linking framing and issue attention cycle toward an integrated theory of print news coverage of epidemics. Mass Communication & Society, 11(2), 141-160. Sheluchin, A., Johnston, R. M., & van der Linden, C. (2020). Public responses to policy reversals: the case of mask usage in Canada during COVID-19. Canadian Public Policy, 46(S2),S119-S126. Siegrist, M., & Árvai, J. (2020). Risk perception: Reflections on 40 years of research. Risk Analysis, 40(S1), 2191-2206. Siomkos, G. J., & Kurzbard, G. (1994). The hidden crisis in product‐harm crisis management. European Journal of Marketing, 28(2), 30-41. Sørensen, M., & Evensen, D. (2023). Framing the Covid‐19 pandemic: A comparative analysis of Swedish and Danish crisis communication. Journal of Contingencies and Crisis Management. Stoddart, M. C., Ramos, H., Foster, K., & Ylä-Anttila, T. (2023). Competing crises? Media coverage and framing of climate change during the COVID-19 pandemic. Environmental Communication, 17(3), 276-292. Warren, G. W., & Lofstedt, R. (2022). Risk communication and COVID-19 in Europe: lessons for future public health crises. Journal of Risk Research, 25(10), 1161-1175. Walker, W. E., Marchau, V. A., & Kwakkel, J. H. (2013). Uncertainty in the framework of policy analysis. Public policy analysis: New developments, 215-261. Weible, C. M., Nohrstedt, D., Cairney, P., Carter, D. P., Crow, D. A., Durnová, A. P., Heikkila, T., Ingold, K., McConnell, A. & Stone, D. (2020). COVID-19 and the policy sciences: initial reactions and perspectives. Policy Sciences, 53, 225-241. Weiner, B. (1985). An attributional theory of achievement motivation and emotion. Psychological Review, 92(4), 548. Weingart, P., Engels, A., & Pansegrau, P. (2000). Risks of communication: discourses on climate change in science, politics, and the mass media. Public Understanding of Science, 9(3), 261. Winblad, U., Swenning, A. K., & Spangler, D. (2022). Soft law and individual responsibility: A review of the Swedish policy response to COVID-19. Health Economics, Policy and Law, 17(1), 48-61. Wu, X., Ramesh, M., & Howlett, M. (2015). Policy capacity: A conceptual framework for understanding policy competences and capabilities. Policy and Society, 34(3-4), 165-171. Yousefinaghani, S., Dara, R., Mubareka, S., Papadopoulos, A., & Sharif, S. (2021). An analysis of COVID-19 vaccine sentiments and opinions on Twitter. International Journal of Infectious Diseases, 108, 256-262. Zhang, Y.S. D., Young, L. H., Sharafaddin-Zadeh, Y., Noels, K., & Lou, N. M. (2021). Public health messages about face masks early in the COVID-19 pandemic: Perceptions of andimpacts on Canadians. Journal of Community Health, 46(5), 903-912.
Description:	碩士國立政治大學行政管理碩士學程 110921034
Source URI:	http://thesis.lib.nccu.edu.tw/record/#G0110921034
Data Type:	thesis
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