學術產出-Theses
Article View/Open
Publication Export
-
題名 網路輿情聲量對高端疫苗施打量的影響
Internet opinion and sentiment on the willingness of getting the MVC covid-19 vaccine作者 楊士逸
Yang, Shih-Yi貢獻者 王信實
Wang, Shinn-Shyr
楊士逸
Yang, Shih-Yi關鍵詞 合成控制法
網路輿情
因果關係
高端疫苗
Synthetic control method
Internet opinion
Causality
MVC covid-19 vaccine日期 2022 上傳時間 1-Aug-2022 18:29:31 (UTC+8) 摘要 本文利用合成控制法 (synthetic control method)，研究網路輿情負面聲量與高端疫苗 (MVC covid-19 vaccine) 施打量之間的因果關係 (causality)。以台灣衛生福利部疾病管制署 (CDC) 提供的疫苗施打期數為基準，在施打區間上做些許的調整，並搭配 OpView 社群口碑資料庫的聲量資料，探討2021年9月24日至10月2日的網路輿情負面聲量介入效果 (treatment effect)，是否對日後的高端疫苗施打量有影響。研究結果顯示，2021年10月3日至10月5日的高端疫苗施打量下降98%，與模擬出來的對照組 (control group) 相比下多減少70%，而10月5日後的高端疫苗施打量也都低於模擬對照組的施打量。
This research studies the causality between the negative public opinion on the internet and the number of MVC covid-19 vaccinated by the synthetic control method (SCM). Based on the vaccine data provided by the Centers for the Disease Control and Prevention (CDC) of the Taiwan Ministry of Health and Welfare, some adjustments are made to the data period in the analysis. Together with the public opinion data of the OpView, this study explores the effect of negative online public opinion on the number of MVC vaccinated from September 24th, 2021 to October 2nd, 2021. The results show a 98% drop in MVC vaccinations between October 3rd and October 5th, 2021, and a 70% reduction compared to the simulated control group. The decrease in the MVC vaccinations has a continuous impact in the later periods.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.Saleska, J., L. & Choi, K., R. (2021), “A behavioral economics. perspective on the COVID-19 vaccine amid public mistrust.”, TBM, 11:821-825.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Cunningham, S. (2021), “Causal inference: The Mixtape”, Yale University.D.Angrist, J. & Pischke, J.S. (2009), “Most Harmless Econometrics: An Empiricist’s Companion”, Princeton University. 描述 碩士
國立政治大學
經濟學系
109258042資料來源 http://thesis.lib.nccu.edu.tw/record/#G0109258042 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) 楊士逸 zh_TW dc.contributor.author (Authors) Yang, Shih-Yi en_US dc.creator (作者) 楊士逸 zh_TW dc.creator (作者) Yang, Shih-Yi en_US dc.date (日期) 2022 en_US dc.date.accessioned 1-Aug-2022 18:29:31 (UTC+8) - dc.date.available 1-Aug-2022 18:29:31 (UTC+8) - dc.date.issued (上傳時間) 1-Aug-2022 18:29:31 (UTC+8) - dc.identifier (Other Identifiers) G0109258042 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/141257 - 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) 109258042 zh_TW dc.description.abstract (摘要) 本文利用合成控制法 (synthetic control method)，研究網路輿情負面聲量與高端疫苗 (MVC covid-19 vaccine) 施打量之間的因果關係 (causality)。以台灣衛生福利部疾病管制署 (CDC) 提供的疫苗施打期數為基準，在施打區間上做些許的調整，並搭配 OpView 社群口碑資料庫的聲量資料，探討2021年9月24日至10月2日的網路輿情負面聲量介入效果 (treatment effect)，是否對日後的高端疫苗施打量有影響。研究結果顯示，2021年10月3日至10月5日的高端疫苗施打量下降98%，與模擬出來的對照組 (control group) 相比下多減少70%，而10月5日後的高端疫苗施打量也都低於模擬對照組的施打量。 zh_TW dc.description.abstract (摘要) This research studies the causality between the negative public opinion on the internet and the number of MVC covid-19 vaccinated by the synthetic control method (SCM). Based on the vaccine data provided by the Centers for the Disease Control and Prevention (CDC) of the Taiwan Ministry of Health and Welfare, some adjustments are made to the data period in the analysis. Together with the public opinion data of the OpView, this study explores the effect of negative online public opinion on the number of MVC vaccinated from September 24th, 2021 to October 2nd, 2021. The results show a 98% drop in MVC vaccinations between October 3rd and October 5th, 2021, and a 70% reduction compared to the simulated control group. The decrease in the MVC vaccinations has a continuous impact in the later periods. en_US dc.description.tableofcontents 第一章 緒論 1第二章 文獻回顧 4第一節 covid-19對社會的影響 4第二節 合成控制法的演進 5第三節 改良後的合成控制法 6第三章 資料介紹 8第一節 OpView社群口碑資料庫 - 聲量資料 8第二節 台灣衛生福利部疾病管制署 - 新冠疫苗資料 9第三節 資料處理 9第四節 介入效果 12第四章 研究方法 16第五章 實證結果 18第一節 變數 18第二節 權重 20第三節 模擬 21第四節 安慰劑檢定 22第六章 結論 24第一節 研究結果與貢獻 24第二節 研究限制與未來研究方向 24參考文獻 26附錄 I zh_TW dc.format.extent 1897826 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0109258042 en_US dc.subject (關鍵詞) 合成控制法 zh_TW dc.subject (關鍵詞) 網路輿情 zh_TW dc.subject (關鍵詞) 因果關係 zh_TW dc.subject (關鍵詞) 高端疫苗 zh_TW dc.subject (關鍵詞) Synthetic control method en_US dc.subject (關鍵詞) Internet opinion en_US dc.subject (關鍵詞) Causality en_US dc.subject (關鍵詞) MVC covid-19 vaccine en_US dc.title (題名) 網路輿情聲量對高端疫苗施打量的影響 zh_TW dc.title (題名) Internet opinion and sentiment on the willingness of getting the MVC 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.Saleska, J., L. & Choi, K., R. (2021), “A behavioral economics. perspective on the COVID-19 vaccine amid public mistrust.”, TBM, 11:821-825.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Cunningham, S. (2021), “Causal inference: The Mixtape”, Yale University.D.Angrist, J. & Pischke, J.S. (2009), “Most Harmless Econometrics: An Empiricist’s Companion”, Princeton University. zh_TW dc.identifier.doi (DOI) 10.6814/NCCU202200959 en_US
