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    題名: 社群災難新聞主題對應用戶情緒與眾議之厚數據分析
    Thick Data Analysis of Social Disaster News Topics Corresponding to User Emotions and Public Opinions
    作者: 王蔓菁
    Wang, Man-Ching
    貢獻者: 許志堅
    王蔓菁
    Wang, Man-Ching
    關鍵詞: 災難新聞
    社群新聞主題
    社群用戶反應行為
    厚數據分析
    日期: 2023
    上傳時間: 2023-09-01 15:45:10 (UTC+8)
    摘要: 本研究旨在分析社群災難新聞中，不同的新聞主題導致的社群用戶留言情 緒與討論議題之差異，進一步以天災與人禍事件新聞對比，了解不同的災難類 型產生的不同用戶反應現象。本研究使用厚數據研究中的人工收集數位足跡編碼方法，研究樣本事件為 2021 至 2022 期間發生的天災與人禍事件，天災事件 為 2022 年尼莎颱風淹水、2022 年台東大地震、2022 高雄雷雨淹水水災，人禍 事件為 2021 年太魯閣列車出軌事故、2021 年高雄城中城火災、2022 年新竹輪 胎行火災。本研究獲取新聞樣本之對象為影響力排行前五的 Facebook 新聞粉絲 專頁《ETtoday 新聞雲》、《東森新聞》、《udn.com 聯合新聞網》、《TVBS 新聞》、《三立新聞》，共收集 641 則新聞樣本數據。
    研究結果顯示，641 則新聞中，責任歸屬的新聞主題更多;用戶情緒類目 中，天災的總統計比例最多之情緒為厭惡情緒，人禍總統計比例最多之情緒為 憤怒情緒;用戶討論議題類目中，天災的總統計比例最多之討論議題為其他議 題，人禍的總統計比例最多之討論議題為制度探討議題。研究發現，單一留言 可能含有零到多種情緒和討論議題;天災新聞沒有準確的咎責對象，留言區立 場混亂，人禍新聞則有咎責對象，用戶立場一致性更高，情緒集中度較天災新 聞更高。總體而言，本研究深入觀察與分析社群災難新聞中，用戶非理性留言 中的情緒，以及用戶理性留言的討論議題，留下專門對於此世代的台灣社群傳 播現象紀錄。
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    描述: 碩士
    國立政治大學
    傳播學院傳播碩士學位學程
    110464050
    資料來源: http://thesis.lib.nccu.edu.tw/record/#G0110464050
    資料類型: thesis
    顯示於類別:[傳播學院傳播碩士學位學程] 學位論文

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