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    題名: 一個研究社群媒體政治活動之計算方法
    A computational approach to the study of political activities on social media
    作者: 邱淑怡
    Chiu, Shu-i
    貢獻者: 徐國偉
    Hsu, Kuo-Wei
    邱淑怡
    Chiu, Shu-i
    關鍵詞: 訊息傳播
    社群媒體
    天際線查詢
    預測
    分類與分群
    文字探勘
    語意分析
    臉書
    太陽花學運
    Information diffusion
    Social media
    Skyline query
    Prediction
    Clustering and classification
    Text mining
    Sentiment analysis
    Facebook
    Sunflower student movement
    日期: 2018
    上傳時間: 2018-07-11 15:27:14 (UTC+8)
    摘要: 資訊與通訊科技帶來社會的變革與人們的便利,已經改變人們的生活模式。Facebook(臉書)、Google、YouTube等社群媒體興起,環繞著人們的日常作息。社群媒體帶來的不僅只是傳播本質的變化,它從政治,經濟,社會系統結構等,全面滲透人們的生活當中,儼然已經成為人們生活的一部分。社群媒體提供使用者方便上傳資訊、分享圖片和影片等訊息的共享平台,每個人都可以成為資訊的創造者或分享者,可以恣意地在社群媒體發表言論及心情,它創造一個訊息傳播的平台。在眾多社群媒體中,臉書的全球使用者超過20億,是擁有最多使用者的社群網站,因此,我們選定臉書做為研究的社群媒體。
    本研究主要是在社群媒體上的政治活動進行全面性的研究,以政治活動之粉絲專頁的貼文進行分析,擷取貼文的互動特徵及文字特徵,依據不同的分析議題提出計算的方法。互動特徵的分析是以太陽花學運的政治活動進行研究,運用不同角度探討熱門貼文,臉書貼文的傳播軌跡,利用這些傳播的軌跡發現重要的臉書使用者。然而,太陽花學運為特定主題之政治活動,屬於短期性政治活動。而文字特徵的探勘需要長期政治活動,且還能包含不同政治立場的黨派才能進行分析,太陽花學運無法滿足我們的研究議題,因此,我們引用美國政黨粉絲專頁貼文為素材,利用美國左、右派政黨在臉書上的貼文,進行貼文黨派傾向的預測分析。而透過粉絲專頁的資料,不具有任何網路結構圖,故本文提出一些新的方法來解決當沒有網路架構下如何進行社群媒體的分析研究。
    本研究運用貼文的互動特徵得到熱門貼文,探討訊息傳播的能力,並在太陽花學運期間找到重要的臉書使用者;利用貼文的互動特徵及文字特徵預測美國左右派貼文內容的政黨傾向,比較不同分類器之預測結果與特徵的影響因素。
    In recent years, due to the rise of social media websites on Internet and the popularity of mobile devices capable of Internet access, people can quickly publish their statuses and messages to social media anytime at any place. Internet has changed our lives; we use Internet in almost everything we do. As of 2017, Facebook had 2 billion monthly active users. It is the most popular social networking platform in the world. Therefore, we choose Facebook as our research platform.
    This dissertation focuses on the analysis of political activities entirely. We use posts of fan pages to analyze political activities and then construct the interaction features and sentiment features of posts on Facebook. We use characteristics of features to analyze political activities. The sunflower student movement focuses on the interaction features. We use methods to search popular posts and to analyze information diffusion. Then, we mine important Facebook users. We get popular posts and find that three users are active and important users through sharing-reaction during the sunflower student movement. However, the sunflower movement cannot investigate the sentiment features because all fan pages fight against the Cross-Strait Service Trade Agreement (CSSTA). For the sentiment features, we study prediction of political tendency of posts. We also collect posts from political groups of fan pages in America; we build sentiment features for the prediction and evaluate prediction performance.
    To summarize, in this dissertation, we propose novel methods to analyze social media datasets that contain valuable information but do not contain any network structure required by other methods.
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    描述: 博士
    國立政治大學
    資訊科學系
    97753506
    資料來源: http://thesis.lib.nccu.edu.tw/record/#G0097753506
    資料類型: thesis
    DOI: 10.6814/DIS.NCCU.CS.002.2018.B02
    顯示於類別:[資訊科學系] 學位論文

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