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題名 COVID-19疫苗施打與網路輿情聲量關係: 以Moderna疫苗為例
How the internet opinion and sentiment influence the willingness of getting vaccinated? Case of the Moderna covid-19 vaccine作者 曾偉恩
TSENG, WEI-EN貢獻者 王信實
Wang, Shinn-Shyr
曾偉恩
TSENG, WEI-EN關鍵詞 網路輿情
聲量
疫苗施打
因果關係
合成控制法
Internet Pubilc Opinion
Volume
Vaccine Injection
Causality
Synthetic Control Method日期 2022 上傳時間 1-Aug-2022 18:29:02 (UTC+8) 摘要 近年來COVID-19大肆傳染，政府積極傳遞疫苗的相關資訊來防止疫情擴散，由於民眾接收COVID-19資訊不只是來自政府，還有一部分來自網路輿情，而網路上存在許多真假難辨的資訊，造就民眾產生施打疫苗的疑慮，使疫苗施打量無法達到政府預期，因此若政府釐清網路資訊與施打量的因果關係，或許能提高疫苗施打量。在此透過合成控制法 (Synthetic Control Method)，使用OpView資料庫的聲量資料，以及衛生福利部疾病管制署提供AstraZeneca、BioNTech、Moderna、Medigen四種疫苗在施打量，發現網路輿情與施打量之間存在相關性後，並嘗試找出其因果關係。
During the COVID-19 pandemic, the governments around the world actively disseminated the vaccine information and promoted the vaccination to prevent the epidemic. The mis- and dis-information about the vaccination on the internet usually makes people worried and thus decreases the willingness of vaccination. By using the Synthetic Control Method and the OpView data, as well as the AstraZeneca, BioNTech, Moderna, and Medigen vaccines provided by Taiwan Centers for Disease Control, this study investigates the negative causal relationship between the internet public opinion and vaccination. It is helpful to increase the number of people vaccinated by clarifying the causal relationship between internet information and vaccination.參考文獻 一、中文文獻：陳宜廷 (2019),“臺灣與南韓之經濟成長比較-合成控制法下的反事實分析”, 臺灣經濟預測與政策(中央研究院經濟研究所), 50(1), 1-410.二、英文文獻：Abadie, A. & J. Gardeazabal (2003), “The Economic Costs of Conflict: A Case Study of the Basque Country”, The American Economic Review, 93, 112–132.Abadie, A., A. Diamond & J. Hainmueller (2010), “Synthetic Control Methods for Comparative Case Studies: Estimating the Effect of California’s Tobacco Control Program”, Journal of the American Statistical Association, 105:490, 493–505.Abadie, A., A. Diamond & J. Hainmueller (2015), “Comparative Politics and the Synthetic Control Method”, American Journal of Political Science, 59, 495–510.Abadie, A. & J. L’Hour (2021), “A Penalized Synthetic Control Estimator for Disaggregated Data”, Journal of the American Statistical Association, 116:536, 1817-1834.Abadie, A. (2021), “Using Synthetic Controls: Feasibility, Data Requirements, and Methodological Aspects”, Journal of Economic Literature, 59(2), 391-425.Ben-Michael, E., A. Feller & J. Rothstein (2021), “The Augmented Synthetic Control Method”, Journal of the American Statistical Association, 116:536, 1789-1803.Chen, Y.-T. (2020), “A distributional synthetic control method for policy evaluation”, Journal of Applied Econometrics, 35, 505-525.Chen, Y.-T. (2022), “Regularization of Synthetic Controls for Policy Evaluation”, Department of Finance National Taiwan University.Doudchenko, N. & G. W. Imbens (2016), “Balancing, Regression, Difference-in-difference and synthetic control methods: A synthesis”, NBER Working Paper.Ferman, B. & C. Pinto (2021), “Synthetic controls with imperfect pretreatment fit”, Quantitative Economics, 12, 1197-1221.Fetzer, T., L. Hensel, J. Hermle & C. Roth (2020), “Coronavirus Perception and Economic Anxiety”, Review of Economics and Statistic, 2021, 103 (5), 968-978.Loomba, S. & A. D. Figueiredo et al. (2021), “Measuring the impact of COVID-19 vaccine misinformation on vaccination intent in the UK and USA”, Nature Human Behaviour, 5, 337-348.Malmendier, U. & S. Nagel (2011),“Depression Babies: Do Macroeconomic Experi- ences Affect Risk Taking?”, The Quarterly Journal of Economics, 126 (1), 373-416.Saleska, J. & L. & Choi, K., R. (2021), “A behavioral economics. perspective on the COVID-19 vaccine amid public mistrust”, TBM, 11, 821-825.Tversky, A. & D. Kahneman (1973), “Availability: A Heuristic for Judging Frequency and Probability”, Cognitive Psychology, 5 (2), 207-232.Valero, R. (2015), “Synthetic Control Method versus Standard Statistical Techniques: a Comparison for Labor Market Reforms”, Working paper, University of Alincante.Vergura, S. (2020), “Bollinger Bands Based on Exponential Moving Average for Statistical Monitoring of Multi-Array Photovoltaic Systems”, Energies, 13, 3992.三、中文書籍伊藤公一朗(王美娟譯) (2018)，《數據分析的力量》，台灣東販。四、英文書籍Cunning, S. (2021), “Causal inference: The Mixtape”, Yale University.Angrist, J. D. & Pischke, J. S. (2009), “Most Harmless Econometrics: An Empiricist’sCompanion”, Princeton University. 描述 碩士
國立政治大學
經濟學系
109258040資料來源 http://thesis.lib.nccu.edu.tw/record/#G0109258040 資料類型 thesis dc.contributor.advisor 王信實 zh_TW dc.contributor.advisor Wang, Shinn-Shyr en_US dc.contributor.author (Authors) 曾偉恩 zh_TW dc.contributor.author (Authors) TSENG, WEI-EN en_US dc.creator (作者) 曾偉恩 zh_TW dc.creator (作者) TSENG, WEI-EN en_US dc.date (日期) 2022 en_US dc.date.accessioned 1-Aug-2022 18:29:02 (UTC+8) - dc.date.available 1-Aug-2022 18:29:02 (UTC+8) - dc.date.issued (上傳時間) 1-Aug-2022 18:29:02 (UTC+8) - dc.identifier (Other Identifiers) G0109258040 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/141255 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 經濟學系 zh_TW dc.description (描述) 109258040 zh_TW dc.description.abstract (摘要) 近年來COVID-19大肆傳染，政府積極傳遞疫苗的相關資訊來防止疫情擴散，由於民眾接收COVID-19資訊不只是來自政府，還有一部分來自網路輿情，而網路上存在許多真假難辨的資訊，造就民眾產生施打疫苗的疑慮，使疫苗施打量無法達到政府預期，因此若政府釐清網路資訊與施打量的因果關係，或許能提高疫苗施打量。在此透過合成控制法 (Synthetic Control Method)，使用OpView資料庫的聲量資料，以及衛生福利部疾病管制署提供AstraZeneca、BioNTech、Moderna、Medigen四種疫苗在施打量，發現網路輿情與施打量之間存在相關性後，並嘗試找出其因果關係。 zh_TW dc.description.abstract (摘要) During the COVID-19 pandemic, the governments around the world actively disseminated the vaccine information and promoted the vaccination to prevent the epidemic. The mis- and dis-information about the vaccination on the internet usually makes people worried and thus decreases the willingness of vaccination. By using the Synthetic Control Method and the OpView data, as well as the AstraZeneca, BioNTech, Moderna, and Medigen vaccines provided by Taiwan Centers for Disease Control, this study investigates the negative causal relationship between the internet public opinion and vaccination. It is helpful to increase the number of people vaccinated by clarifying the causal relationship between internet information and vaccination. en_US dc.description.tableofcontents 摘要 IABSTRACT II目次 III表次 IV圖次 IV第一章 緒論 1第二章 文獻回顧 6第一節 COVID-19衝擊與信念形成 6第二節 網路輿情與疫苗施打意願 6第三節 合成控制模型架構 7第四節 合成控制模型演進 8第三章 資料介紹 10第一節 資料庫來源 10第二節 資料處理 11第四章 研究方法 17第一節 合成控制法 17第二節 模型設定 18第五章 模型結果 19第一節 合成結果 19第二節 安慰劑檢定 23第六章 結論 25第一節 主要研究結果與貢獻 25第二節 研究限制與未來研究方向 25參考文獻 27附錄 1 zh_TW dc.format.extent 1820657 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0109258040 en_US dc.subject (關鍵詞) 網路輿情 zh_TW dc.subject (關鍵詞) 聲量 zh_TW dc.subject (關鍵詞) 疫苗施打 zh_TW dc.subject (關鍵詞) 因果關係 zh_TW dc.subject (關鍵詞) 合成控制法 zh_TW dc.subject (關鍵詞) Internet Pubilc Opinion en_US dc.subject (關鍵詞) Volume en_US dc.subject (關鍵詞) Vaccine Injection en_US dc.subject (關鍵詞) Causality en_US dc.subject (關鍵詞) Synthetic Control Method en_US dc.title (題名) COVID-19疫苗施打與網路輿情聲量關係: 以Moderna疫苗為例 zh_TW dc.title (題名) How the internet opinion and sentiment influence the willingness of getting vaccinated? Case of the Moderna covid-19 vaccine en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) 一、中文文獻：陳宜廷 (2019),“臺灣與南韓之經濟成長比較-合成控制法下的反事實分析”, 臺灣經濟預測與政策(中央研究院經濟研究所), 50(1), 1-410.二、英文文獻：Abadie, A. & J. Gardeazabal (2003), “The Economic Costs of Conflict: A Case Study of the Basque Country”, The American Economic Review, 93, 112–132.Abadie, A., A. Diamond & J. Hainmueller (2010), “Synthetic Control Methods for Comparative Case Studies: Estimating the Effect of California’s Tobacco Control Program”, Journal of the American Statistical Association, 105:490, 493–505.Abadie, A., A. Diamond & J. Hainmueller (2015), “Comparative Politics and the Synthetic Control Method”, American Journal of Political Science, 59, 495–510.Abadie, A. & J. L’Hour (2021), “A Penalized Synthetic Control Estimator for Disaggregated Data”, Journal of the American Statistical Association, 116:536, 1817-1834.Abadie, A. (2021), “Using Synthetic Controls: Feasibility, Data Requirements, and Methodological Aspects”, Journal of Economic Literature, 59(2), 391-425.Ben-Michael, E., A. Feller & J. Rothstein (2021), “The Augmented Synthetic Control Method”, Journal of the American Statistical Association, 116:536, 1789-1803.Chen, Y.-T. (2020), “A distributional synthetic control method for policy evaluation”, Journal of Applied Econometrics, 35, 505-525.Chen, Y.-T. (2022), “Regularization of Synthetic Controls for Policy Evaluation”, Department of Finance National Taiwan University.Doudchenko, N. & G. W. Imbens (2016), “Balancing, Regression, Difference-in-difference and synthetic control methods: A synthesis”, NBER Working Paper.Ferman, B. & C. Pinto (2021), “Synthetic controls with imperfect pretreatment fit”, Quantitative Economics, 12, 1197-1221.Fetzer, T., L. Hensel, J. Hermle & C. Roth (2020), “Coronavirus Perception and Economic Anxiety”, Review of Economics and Statistic, 2021, 103 (5), 968-978.Loomba, S. & A. D. Figueiredo et al. (2021), “Measuring the impact of COVID-19 vaccine misinformation on vaccination intent in the UK and USA”, Nature Human Behaviour, 5, 337-348.Malmendier, U. & S. Nagel (2011),“Depression Babies: Do Macroeconomic Experi- ences Affect Risk Taking?”, The Quarterly Journal of Economics, 126 (1), 373-416.Saleska, J. & L. & Choi, K., R. (2021), “A behavioral economics. perspective on the COVID-19 vaccine amid public mistrust”, TBM, 11, 821-825.Tversky, A. & D. Kahneman (1973), “Availability: A Heuristic for Judging Frequency and Probability”, Cognitive Psychology, 5 (2), 207-232.Valero, R. (2015), “Synthetic Control Method versus Standard Statistical Techniques: a Comparison for Labor Market Reforms”, Working paper, University of Alincante.Vergura, S. (2020), “Bollinger Bands Based on Exponential Moving Average for Statistical Monitoring of Multi-Array Photovoltaic Systems”, Energies, 13, 3992.三、中文書籍伊藤公一朗(王美娟譯) (2018)，《數據分析的力量》，台灣東販。四、英文書籍Cunning, S. (2021), “Causal inference: The Mixtape”, Yale University.Angrist, J. D. & Pischke, J. S. (2009), “Most Harmless Econometrics: An Empiricist’sCompanion”, Princeton University. zh_TW dc.identifier.doi (DOI) 10.6814/NCCU202200980 en_US
