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博碩士論文 etd-0108121-143045 詳細資訊
Title page for etd-0108121-143045
論文名稱
Title
2016年立法委員選舉區域候選人臉書貼文對選民心理及回應行為的影響
The Influence of Legislative Candidates' Facebook Posts on Voters' Response Behavior in the 2016 Legislative Election
系所名稱
Department
畢業學年期
Year, semester
語文別
Language
學位類別
Degree
頁數
Number of pages
124
研究生
Author
指導教授
Advisor
召集委員
Convenor
口試委員
Advisory Committee
口試日期
Date of Exam
2021-01-26
繳交日期
Date of Submission
2021-02-08
關鍵字
Keywords
議題設定、社群媒體、政治傳播、我群建構、自我揭露、情緒分析
agenda setting, political communication, sentiment analysis, social media, community building, self-disclosure
統計
Statistics
本論文已被瀏覽 144 次,被下載 54
The thesis/dissertation has been browsed 144 times, has been downloaded 54 times.
中文摘要
在台灣,眾多立委候選人都會選擇在臉書(Facebook)這個最多人使用的社群平台上開設公開的粉絲專頁,除了分享選戰資訊、宣傳形象、各類公共議題及政治述求外,也會藉由表露正/負面情感的貼文來喚起網民的共感。此外,自我揭露私人生活的貼文和使用第一人稱複數代名詞構建群體的文字,也是拉近和網民之間親密感的策略。本研究採用電腦內容分析法,探勘候選人的貼文是否透過微觀層次的理性認知、正負面情緒和親密感等心理機制的中介,影響網友的回覆行為(例如:按讚、留言和分享),最終達到議題設定、情緒傳染、自我揭露和我群建構的效果。
本研究使用Netvizz軟體抓取立法委員候選人於2016年選舉前三個月在臉書的貼文及留言,經過R語言Jieba套件斷詞後,再以中文版的LIWC詞典(C-LIWC)計算貼文和留言中目標字詞的占比,用以分析貼文與留言在各個變項間的因果關係。研究結果發現,候選人貼文的理性訴求和焦慮情緒字詞會激起網民的理性認知,而感性訴求的正面情緒貼文會激起網民的正面情緒、負面情緒貼文會激起網民的負面情緒。再者,私人生活自我揭露的貼文可以增加網民對候選人的親密感知,但使用第一人稱複數代名詞構建我群則不能。最後,含有正面情緒、負面情緒、私人生活自我揭露、我群建構的貼文都會增加回覆行為的按讚數量,但只有負面情緒貼文受網民心理機制的負面情緒所中介。
Abstract
The establishment and maintenance of a public Facebook fan page have become one of the most popular strategies of promotion for Taiwanese legislative election candidates, as the fan page not only provides a platform for them to share information their political appeals, promote their self-image, and discuss public issues, but also allows them to post articles involving positive or negative sentiment and incite public empathy. In addition, the portrayal of one’s personal life on the fan page and the use of first person plural pronoun in posts to construct the feeling of community have also been available strategies to boost intimacy with voters. This study adopted computer assisted content analysis to explore whether articles on candidates’ fan pages can affect voters’ Facebook response or actions (i.e. “like”, comment, or share) through the mediation of micro-level mental mechanisms, such as rational cognition, sentiment and intimacy, to achieve the effects of agenda setting, sentiment diffusion, self-disclosure and community building.
The data collection included posts, comments, number of likes, comments, shares on fan pages of candidates fetched by software Netvizz. The articles posted within three months period prior to the legislative election in 2016 were fetched for each candidate.For data analysis, the Jieba package was incorporated in R to segment sentences, and then the ratio of target words was counted via the Chinese version of LIWC (C-LIWC) dictionary. The casual relationships among posts’ variables and comments’ variables were analyzed.
The results have shown that rational appeals and anxious wordings in candidates’ posts can elicit voters’ rational cognition. Also, sentimental appeals of positive posts can generate positive sentiment while negative posts can generate negative sentiment. Moreover, self-disclosure in posts can increase voter-candidate intimacy, but no effect is achieved by community building with the use of first-person plural pronouns. Although positive wordings, negative wordings, self-disclosure, and community building in post can all enhance the “like” counts, but only posts with negative wordings are mediated by mental mechanism.
目次 Table of Contents
論文審定書 i
誌謝 ii
摘要 iii
Abstract iv
第一章 緒論 1
第一節 研究背景 1
第二節 研究動機 3
第三節 研究問題 5
第四節 研究目的 6
第二章 文獻探討 7
第一節 社群媒體對政治參與的影響 7
一、候選人使用臉書原因 7
二、網民使用臉書的政治參與 9
第二節 候選人貼文理性訴求對網民認知處理的影響 12
第三節 候選人貼文感性訴求對網民的影響 14
一、情感傳染效果 14
二、情感與行為決策 16
第四節 候選人貼文的自我揭露效果 19
第五節 我群建構的文字影響網民親密度 21
第三章 研究方法 24
第一節 研究架構與假說 24
第二節 分析方法 25
一、電腦內容分析法 25
二、中文版LIWC詞典 26
三、變項定義 30
四、測量變數方式 33
五、控制變數 34
六、樣本處理與分析 35
第四章 資料分析與結果 38
第一節 立委候選人臉書樣本描述性統計 38
第二節 假說驗證 41
一、假說一與研究問題一 41
二、假說二與研究問題二 46
三、假說三 50
四、假說四 55
五、假說五 60
六、假說六 64
七、候選人貼文內容對網民回應行為的多元迴歸 68
第三節 統計結果小結 74
第五章 總結與未來研究 77
第一節 研究總結 77
第二節 研究貢獻 80
第三節 研究限制 81
參考文獻 82
中文文獻 82
英文文獻 84
附錄一、R語言程式碼 101
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