臉書粉絲專頁互動與選舉結果之相關性研究

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姓名

黃琮閔(CONG-MIN HUANG) 查詢紙本館藏

畢業系所

資訊管理學系

論文名稱

臉書粉絲專頁互動與選舉結果之相關性研究

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摘要(中)

社群媒體對選舉的影響扮演越來越重要的角色，本研究旨在提出一個用於解釋得票率所受影響之整合模型，並分析不同屬性之線上聲量指標與選舉結果的相關性，且依據實証結果詮釋社群媒體活躍程度與選舉結果的關係。本研究以2018年台灣直轄市長選舉19位候選人為研究對象，分析其1,137篇Facebook貼文之互動資料，並以縱慣性研究的概念為基礎，在指標設計中加入時間跨度，使指標能夠反映隨時間變化的互動量，一共設計了42個指標。研究結果顯示「所有貼文按讚數總和」、「平均粉專按讚成長」、「平均粉專追蹤成長」、「最大單篇貼文按讚數」以及「平均每篇貼文按讚數」是對得票率最具影響力之前五大指標，且整體上按讚的影響力大於分享大於留言。此結果可供未來學者與候選人設計社群媒體策略時之重要參考。

摘要(英)

The impact of social media on elections is playing an increasingly important role. Our research proposes an integrated model extracting the key impact factor of vote shares and analyzing the correlation between online popularity and election results. This research resolves the relationship between social media engagement and election outcome based on empirical results. We take 19 candidates from the mayor election of Taiwan in 2018 as the research object, analyzes social media data from 1,137 Facebook posts, and adds time span to the index design based on the concept of longitudinal research. With this measurement, we adopt 42 indicators which can reflect time-varying interactions. A total of 42 indicators were designed. Our research shows that “Sum of all posts likes”, “Average growth of fan page likes”, “Average growth of fan page follows”, “Max single post likes” and "Average post likes” are the top five indicators that have the most influence on vote shares, and the overall influence of likes is greater than shares and greater than the comments. Our research provides a new integrated model which employ the longitudinal measurement and extract top five social media impact factor of elections which are indispensable groundworks for future researches.

關鍵字(中)

★ 社群媒體
★ 政治參與
★ 選舉預測

關鍵字(英)

★ Social media
★ Facebook
★ Political participation
★ Election forecast

論文目次

摘要 i
Abstract ii
目錄 iii
圖目錄 v
表目錄 vi
一、緒論 1
1-1 研究動機 1
1-2 研究目的 2
二、文獻探討 3
2-1 線上聲量 3
2-2 線下聲量 5
2-3 其他因素 5
2-4 小結 6
三、研究方法 8
3-1 研究架構 8
3-2 資料與定義 9
3-2-1 資料來源 12
3-2-2 社群活躍度指標 12
3-2-3 指標說明 13
3-3 資料蒐集與前處理 16
3-4 分析方法 16
3-4-1 單變量迴歸分析 16
3-4-2 多變量迴歸分析 16
四、結果與討論 17
4-1 敘述性統計 17
4-2 結果 18
4-2-1 結果一：單變量迴歸分析 18
4-2-2 結果二：多變量迴歸分析 21
4-2-3 討論一：單變量迴歸分析 22
4-2-4 討論二：各互動類別內部排名 24
4-2-5 討論三：線上社群媒體指標預測力分析 27
4-2-6 討論四：多個指標之預測力分析 28
五、結論與建議 30
5-1 文獻比較 30
5-2 學術貢獻 31
5-3 實務意涵 32
5-4 研究限制與建議 33
參考文獻 34
附錄一：候選人Facebook粉絲專頁 37
附錄二：多變量迴歸分析結果 38

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許文錦

審核日期

2019-7-12

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