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博碩士論文 etd-0102123-100050 詳細資訊
Title page for etd-0102123-100050
論文名稱
Title
國會政黨臉書針對新冠肺炎「責難式」發文之比較-以BERT模型分析第九屆、第十屆立法院
A Comparison of the Blaming Texts on Facebook in Response to COVID-19 Posted by Legislators from Different Parties - An Analysis of the 9th and 10th Legislative Yuan with the BERT Model
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
86
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
2023-01-11
繳交日期
Date of Submission
2023-02-02
關鍵字
Keywords
新冠肺炎、責難、民主韌性、聚旗效應、BERT、情緒預測
COVID-19, Blame, Democratic Resilience, Rally ‘round the flag effect, BERT, Sentiment Analysis
統計
Statistics
本論文已被瀏覽 225 次,被下載 12
The thesis/dissertation has been browsed 225 times, has been downloaded 12 times.
中文摘要
近年新冠肺炎的肆虐,促使政府執行嚴格防疫措施,惟此舉雖然有助於抑制疫情的傳播,但其中卻也隱藏侵害基本人權的疑慮與行政擴張的風險。而在此背景之下,也衍伸出一個重要問題,即反對黨是否在國家面臨危機時,會傾向與政府合作,降低責難?還是會更為責難政府?而執政黨的國會議員又會配合施政,不加責難嗎?第二,在疫情時期,當疫情走向「嚴峻」或「趨緩」時,國會議員(尤以反對黨)對政府的責 難是否會隨疫情的嚴峻或趨緩而有增加或減少?

基於上述的疑問,本文將以聚旗效應(Rally ‘round the flag effect) 與民主韌性(Democratic Resilience) 作為理論基礎。並以第九屆與第十屆立法委員的臉書貼文為分析資料,透過Keras介面下的BERT模型,進行情緒分析(Sentiment Analysis),藉此比較非疫情時期與疫情時期立法委員對政府的責難變化。並於最後使用迴歸分析來檢證政黨是否為預測責難性的重要因素,以及不同獨立變數與責難性之間的影響。

研究發現,執政黨能更符合本文所欲探討的疫情下聚旗效應理論與民主韌性理論之期待,即面臨危機時會,執政黨籍立委會傾向共同對抗,降低責難,並在危機趨緩後,回復至以往的責難性。而在野黨雖然未出現聚齊效應的效果,但仍展現民主韌性的一面,即不論面臨危機之嚴峻或趨緩時,皆是加強責難,甚至比嚴峻時期有更高的責難表現,以盡監督一職,保持民主體制的機能。
Abstract
Since Covid-19 outbroke, measures and quarantine implemented by Taiwan government had effectively prevented the spread of virus. However, there are concerns about the violations of human rights and the expansion of administrative power. A significant question arises: when the society was facing a crisis, would opposition parties tend to collaborate with the government and reduce blame for the loss in pandemic, or would they be more critical to the government? On the other hand, whether would the legislators of the ruling party collaborate with the government more coordinately? or whether would the legislators of opposition parties impose less blame on the government when the pandemic was mitigated?

Based on the questions above, this thesis will focus how the legislators condemned government during two periods repsectively: before and after Covid-19 outbreak. On the theoretical basis of Rally ‘round the flag effect and Democratic Resilience, we conducted sentimental analysis on data from the 9th and 10th legislators’ Facebook posts using BERT model of Keras to quantitively study the change in legislators’ blame between the two periods. Then, we utilize regression method to examine how political party was an important factor to the severity of blame on the government, and to investigate the correlation between blame and different independent variables.

The discovery from our study, the legislators of the ruling party inclined to comply with the expectations of the theories, Rally ‘round the flag effect and Democratic Resilience. This result implies that the legislators of the ruling party were more willing to collaborate with and reduced blame on the government when facing crisis, and then, after crisis was relieved, returned to the same criticism to the government as previous. Although opposition party members did not show Rally ‘round the flag effect, they displayed the spirit of Democratic Resilience, which means they blame on the government as usual regardless of in crisis or not, or even blame more severely in crisis. This can be viewed as the fulfillment of supervision, maintaining the completeness of democracy function.
目次 Table of Contents
論文審定書 i
中文摘要 ii
Abstract iii
目錄 v
圖次 vii
表次 viii
第一章、緒論 1
1.1研究背景及動機 1
1.2研究目的 3
第二章、文獻回顧 4
2.1民主制度下的政黨互動 4
2.2危機時期的政黨互動與聚旗效應 7
2.3民主韌性 10
2.4國會議員與社群媒體 12
2.5危機時期與社群媒體的運用 14
2.6責難的意義 15
2.7國會議員在臉書上的發言 15
2.8政治學領域的資訊技術應用 18
第三章、研究方法 19
3.1研究假設 20
3.2研究對象與時間範圍 21
3.3研究方法與工具 23
3.3.1疫情危機的嚴峻與趨緩之衡量及時間範圍 23
3.3.2責難式發言的定義 25
3.3.3 BERT模型 29
3.3.4 資料處理流程 30
3.3.5 線性迴歸分析模型 31
3.5 研究限制 32
第四章、資料統計分析與模型分析 33
4.1非疫情時期與疫情時期的責難表現變化 33
4.2疫情「嚴峻」與「趨緩」時期的責難表現變化 34
4.2.1以確診人數為衡量基準 34
4.2.2以警戒級數為衡量基準 37
4.2.3責難性之迴歸分析 39
第五章、結論 46
5.1 研究發現 46
5.2 研究建議與展望 50
參考文獻 52
附錄一:第九屆、第十屆立委臉書資料名單 72
附錄二:BERT二元分類模型Python語法 77
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