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研究生:羅莉婷
研究生(外文):LO, LI-TING
論文名稱:選舉預測方法比較:社群大數據與民意調查實證研究
論文名稱(外文):Comparison of Election Prediction Methods: An Empirical Study of Social Big Data and Polls
指導教授:劉嘉薇劉嘉薇引用關係
指導教授(外文):LIU, JIA-WEI
口試委員:游清鑫蔡奇霖
口試委員(外文):Yu, Ching-HsinTsai, Chi-lin
口試日期:2020-09-22
學位類別:碩士
校院名稱:國立臺北大學
系所名稱:公共行政暨政策學系碩士在職專班
學門:社會及行為科學學門
學類:公共行政學類
論文種類:學術論文
論文出版年:2020
畢業學年度:109
語文別:中文
論文頁數:295
中文關鍵詞:選舉預測社群媒體大數據民意調查總統選舉
外文關鍵詞:election forecastsocial mediabig datapollspresidential election
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預測獲勝者一直係選舉過程最受熱議話題,而民意調查則是探詢投票意向較常見測量方式,然民調預測失準案例頻傳,又面臨如家戶電話回應率低、「唯手機族」人口增加及年輕族群涵蓋率不足等調查誤差問題,加上社群媒體逐漸影響政治及選舉活動,促使國外紛紛興起運用社群大數據預測選舉浪潮,亦已累積相當豐碩文獻,回顧國內卻缺乏較完整、系統性發展。因此,本文試以我國2020年總統大選為個案,探討社群大數據方法應用於臺灣選舉可行性,經檢閱文獻共彙整出測量社群民意三大變數(9個指標)納入分析架構:「數量」、「文本情緒」及「社群用戶特性描述」等相關變數,並採取量化研究方法,同時觀察各社群指標與民調在不同選舉時期預測趨勢,最後比較兩者預測誤差變化,總共獲得185次預測結果,並進一步透過「整合途徑」模式,嘗試結合各社群指標及傳統民調與社群大數據兩種不同類型資料比較分析。
綜觀本研究發現,主要以「社群用戶特性描述」相關指標表現與最後選舉得票數具較大關聯,其中「臉書粉絲專頁貼文按讚數」變數之總體平均絕對誤差最小,且優於民調測量誤差,成為本次社群大數據預測選舉最具指標性變數。另外,大部分經整合後之測量變數預測表現,確實相對單一變數預測成效好,惟傳統民調與社群大數據兩者資料合併預測結果不如原先預期,極可能受到社群用戶、民調受訪者與實際選民等人口特性差異而影響預測成效,一方面也透露出社群大數據方法目前最大挑戰-即如何回應社群用戶代表性問題。
基於上述研究結果,茲提出幾點建議作為未來研究延伸:首先,增加時間權重方法,瞭解選民對近期選舉事件關注程度;其次,依社群用戶人口特性加權處理,改善社群用戶與實際選民之間差異;第三,探討調查方法精進以及使用社群網絡分析方法,並針對不同方法進行廣泛性跨國比較。最後,本研究並非關注在線上社群大數據能否取代傳統民調方法,而是期望藉由此種間接、非侵擾模式來洞察選民真實態度,作為補充、改善選舉民調偏誤情形,且即時又快速的另一種衡量民意方式。
Predicting who was the winner was always the hottest topic in the election process, and survey was a more common way of measuring voting intentions. However, there were frequent cases of inaccurate predictions in the polls, as well as survey errors such as the low response rate of household calls, the increase of “cell phone only” population increase, and the insufficient coverage of young group. Moreover, social media had an gradual influence on politics and election activities. In foreign countries, had prompted the use of social big data to predict election, also accumulated quite a wealth of literature.
However, there was a lack of complete and systematic development in Taiwan. Therefore, this thesis attempts to take Taiwan 2020 presidential election as a case study to explore the feasibility of applying social big data to Taiwan’s elections. After reviewing the literature, we had compiled three major variables (9 indicators) that measure social media public opinion into the analysis framework: “Volume”, “Sentiment”, “Profile Characteristics of Social Users” related variables, adopting quantitative research, and observing the forecast trends of various social media indicators and polls in different election periods. Finally, we compared the changes in the forecast errors between the two, and obtained a total of 185 prediction results, and further through the “integrated approach” model, trying to combine various social media indicators and traditional polls and social big data two different types of data comparative analysis.
Looking at this research, it was found that the performance of indicators related to “Profile Characteristics of Social Users” had a greater correlation with the number of votes. Among them, the overall average absolute error of the variable “number of Likes per post from Facebook” was the smallest, and better than the measurement error of the polls. It has become the most representative variable in this social big data prediction election. In addition, most of the integrated measurement variable prediction performance was indeed better than the single variable prediction. However, the combination of polls and social big data was not as good as expected, and the prediction result was very likely to be affected by social media users, and the difference in demographic characteristics between interviewers and actual voters. On the one hand, it also revealed the biggest challenge of the social big data method-how to respond to the problem of social media users representativeness.
Based on the above research results, I propose several suggestions as an extension of future research: First, increase the time weight method to understand the degree of voters attention to recent election events; Second, weight based on the demographic characteristics of social media users to improve the difference between social media users and actual voters; Third, refinement of survey methods and used the social network analysis methods, and made extensive transnational comparisons for different methods. Finally, this research was not concerned with whether social big data can replace polls, but hopes to use this indirect, non-intrusive model to gain insights into the true attitudes of voters, as a supplement, to improve the situation of election polling bias, and another way to measure public opinion instantly and quickly.
目錄
第一章 緒論 1
第一節 研究背景 1
第二節 研究動機、研究目的及研究問題 3
第二章 理論基礎與文獻回顧 9
第一節 選舉預測與民意調查 9
第二節 大數據研究方法 18
第三節 選舉預測與社群大數據 23
第四節 社群大數據之測量變數應用 37
第五節 小結 47
第三章 研究設計 51
第一節 研究架構與研究假設 51
第二節 研究資料來源與研究方法 56
第四章 研究分析與結果 81
第一節 各自變數原始數據統計分析與趨勢 81
第二節 各自變數預測結果與比較 123
第三節 整合社群大數據及民調資料分析結果 218
第四節 小結 235
第五章 結論與建議 245
第一節 研究發現與討論 245
第二節 研究限制與建議 257
參考書目 261
附錄一:社群大數據於選舉預測研究綜整(依研究年代排序) 275
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