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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 謝吉隆 | zh_TW |
| dc.contributor.advisor | Ji-Lung Hsieh | en |
| dc.contributor.author | 孫嘉君 | zh_TW |
| dc.contributor.author | Chia-Chun Sun | en |
| dc.date.accessioned | 2023-07-19T16:25:55Z | - |
| dc.date.available | 2023-11-09 | - |
| dc.date.copyright | 2023-07-19 | - |
| dc.date.issued | 2023 | - |
| dc.date.submitted | 2023-04-24 | - |
| dc.identifier.citation | 江靜之（2001）。公共領域在網路：公共領域考驗網路、網路創造公共領域。網際網路2001年研討會論文。國立清華大學社會學研究所。
李弘繹（2021）。負面黨性在臺灣：2004-2020 年臺灣總統選舉的分析（碩士論文）。國立臺灣大學政治學研究所。 林宇玲（2014）。網路與公共領域：從審議模式轉向多元公眾模式。新聞學研究， 118，55-85。 林昆賢、蔡俊明（2019）。基於深度學習的自然語言處理中預訓練Word2Vec模型的研究。國教新知，66(1)，15-31。 林思平（2020）。純粹社群，掛釘社群與網絡個人主義：以批踢踢八卦板（PTT Gossiping）為例。資訊社會研究，38，127-161。 張如嫻（2021）。金魚缸裡的隱形風暴：疫情直播記者會下，新聞工作的挑戰與突圍（碩士論文）。國立臺灣大學新聞研究所。 張晨昱（2022）。兩岸媒體報導差異研究——以大陸地區COVID-19疫苗為例（碩士論文）。國立政治大學傳播學院傳播碩士學位學程。 許良亦（2022）。負面黨性與民主去鞏固化: 以 2008-2020 年臺灣為例（碩士論文）。國立臺灣大學政治學研究所。 温煜鈞（2020）。以文件探勘技術探討台灣新聞之媒體偏見現象 -以新冠肺炎議題為例（碩士論文）。國立清華大學服務科學研究所。 黃厚銘、林意仁（2013）。流動的群聚（mob-ility）：網路起鬨的社會心理基礎。新聞學研究，115，1-50。 楊意菁（2006）。民意概念的意涵與變遷：從理性思辯到符號論述建構，傳播研究的傳承與創新，33-58。世新大學。 楊意菁（2008）。網路民意的公共意涵：公衆，公共領域與溝通審議。中華傳播學刊，14，115-167。 蔡榮峰、陳詠君（2021）。激化情緒對立的口水戰- PTT八卦板疫情輿論分析。財團法人國防安全研究院。 鄭宇君、陳百齡（2014）。探索2012年台灣總統大選之社交媒體浮現社群：鉅量資料分析取徑。新聞學研究，120，121-166。 蕭怡靖（2019）。台灣民衆的黨性極化及其對民主態度的影響。台灣政治學刊，23(2)，41-85。 羅晉（2010）。線上「理想言談情境」有多理想？蘇花國道論壇的分析。行政暨政策學報，51，125-170。 Abramowitz, A. I., & Webster, S. (2016). The rise of negative partisanship and the nationalization of US elections in the 21st century. Electoral Studies, 41, 12-22. Abramowitz, A. I., & Webster, S. W. (2018). Negative partisanship: Why Americans dislike parties but behave like rabid partisans. Political Psychology, 39, 119-135. Abramowitz, A., & McCoy, J. (2019). United States: Racial resentment, negative partisanship, and polarization in Trump’s America. The ANNALS of the American Academy of Political and Social Science, 681(1), 137-156. Adnan, M. M. J., Hemmje, M. L., & Kaufmann, M. A. (2021). Social Media Mining to Study Social User Group by Visualizing Tweet Clusters using Word2Vec, PCA and K-Means. In BIRDS+ WEPIR@ CHIIR, 40-51. Albrecht, S. (2006). Whose voice is heard in online deliberation?: A study of participation and representation in political debates on the internet. Information, Community and Society, 9(1), 62-82. Barberá, P., Boydstun, A. E., Linn, S., McMahon, R., & Nagler, J. (2021). Automated text classification of news articles: A practical guide. Political Analysis, 29(1), 19-42. Barde, B. V., & Bainwad, A. M. (2017). An overview of topic modeling methods and tools. In 2017 International Conference on Intelligent Computing and Control Systems (ICICCS), 745-750. IEEE. Bauman, Z., & May, T. (2001). Thinking sociologically. Oxford, UK: Blackwell. Bell, D. (2007). Webs as pegs. In D. Bell (Ed.), The cybercultures reader, 254-263. New York, NY: Routldge. Benson, P. (2015). YouTube as text: Spoken interaction analysis and digital discourse. In Discourse and Digital Practices, 93-108. Routledge. Bianchi, F., Terragni, S., & Hovy, D. (2020). Pre-training is a hot topic: Contextualized document embeddings improve topic coherence. arXiv preprint arXiv:2004.03974. Bimber, B., & Gil de Zúñiga, H. (2020). The unedited public sphere. New Media & Society, 22(4), 700-715. Bodrunova, S. S., Blekanov, I., Smoliarova, A., & Litvinenko, A. (2019). Beyond left and right: real-world political polarization in Twitter discussions on inter-ethnic conflicts. Media and Communication, 7, 119-132. Bossetta, M., Segesten, A. D., & Trenz, H. J. (2018). Political participation on Facebook during Brexit: Does user engagement on media pages stimulate engagement with campaigns?. Journal of Language and Politics, 17(2), 173-194. Boyd, D., & Heer, J. (2006). Profiles as conversation: Networked identity performance on Friendster. In Proceedings of the 39th Annual Hawaii International Conference on System Sciences. IEEE. Boyd, M. S. (2014). (New) participatory framework on YouTube? Commenter interaction in US political speeches. Journal of Pragmatics, 72, 46-58. Burgess, J., & Green, J. (2009). YouTube: Online video and participatory culture. Polity Press. Cambridge. Caruana, N. J., McGregor, R. M., & Stephenson, L. B. (2015). The power of the dark side: Negative partisanship and political behaviour in Canada. Canadian Journal of Political Science/Revue Canadienne de Science Politique, 48(4), 771-789. Cassese, E. C. (2021). Partisan dehumanization in American politics. Political Behavior, 43(1), 29-50. Cesca B. (2011). Bush looks an awful lot like a monkey. http://www.huffingtonpost.com/bob-cesca/bush-looks-an-awful-lot-l_b_14266.html Chang, C. (2022). Cross-country comparison of effects of early government communication on personal empowerment during the COVID-19 pandemic in Taiwan and the United States. Health Communication, 37(4), 476-489. Chau, C. (2010). YouTube as a participatory culture. New Directions for Youth Development, 2010(128), 65-74. Chen, Y., Zhang, H., Liu, R., Ye, Z., & Lin, J. (2019). Experimental explorations on short text topic mining between LDA and NMF based Schemes. Knowledge-Based Systems, 163, 1-13. Cheng, K., Inzer, S., Leung, A., Shen, X., Perlmutter, M., Lindstrom, M., Chew, J., Presner, T. & Needell, D. (2022). Multi-scale Hybridized Topic Modeling: A Pipeline for Analyzing Unstructured Text Datasets via Topic Modeling. arXiv preprint arXiv:2211.13496. Cialdini, R. B., Reno, R. R., & Kallgren, C. A. (1990). A focus theory of normative conduct: Recycling the concept of norms to reduce littering in public places. Journal of Personality and Social Psychology, 58(6), 1015. Cook, H. V., & Jensen, L. J. (2019). A guide to dictionary-based text mining. In Bioinformatics and Drug Discovery, 73-89. Humana Press, New York, NY. Curto, G., Kiritchenko, S., Nejadgholi, I., & Fraser, K. C. (2023). The crime of being poor. arXiv preprint arXiv:2303.14128. Davies L. (2013, Jul. 14). Italian senator says Black minister has “features of orangutan.” http://www.theguardian.com/world/2013/jul/14/italian-senator-roberto-calderoli-cecile-kyenge Davis, R. (1999). The Web of politics: The Internet’s impact on the American political system. Oxford: Oxford University Press. Durkheim, E. (1915). The elementary forms of the religious life, trans. Joseph Ward Swain (New York, 1915). Ebeling, R., Sáenz, C. A. C., Nobre, J. C., & Becker, K. (2022). Analysis of the influence of political polarization in the vaccination stance: the Brazilian COVID-19 scenario. In Proceedings of the International AAAI Conference on Web and Social Media, 16(1), 159-170. Egger, R., & Yu, J. (2022). A topic modeling comparison between lda, nmf, top2vec, and bertopic to demystify twitter posts. Frontiers in Sociology, 7, 886498. Fraser, N. (1990). Rethinking the public sphere: A contribution to the critique of actually existing democracy. Social Text, 25/26, 56-80. Garrett, R. K. (2009). Echo chambers online?: Politically motivated selective exposure among Internet news users. Journal of Computer-Mediated Communication, 14(2), 265-285. Godbole, S., Bhattacharya, I., Gupta, A., & Verma, A. (2010). Building re-usable dictionary repositories for real-world text mining. In Proceedings of the 19th ACM International Conference on Information and Knowledge Management,1189-1198. Grootendorst, M. (2022). BERTopic: Neural topic modeling with a class-based TF-IDF procedure. arXiv preprint arXiv:2203.05794. Guo, J., & Fussell, S. R. (2020). A preliminary study of emotional contagion in live streaming. In Conference Companion Publication of the 2020 on Computer Supported Cooperative Work and Social Computing, 263-268. Habermas, J. (1989). The structural transformation of the public sphere: An inquiry into a category of bourgeois society. Boston, MA: MIT Press. Haider, M. M., Hossin, M. A., Mahi, H. R., & Arif, H. (2020). Automatic text summarization using gensim word2vec and k-means clustering algorithm. In 2020 IEEE Region 10 Symposium (TENSYMP), 283-286. IEEE. Halpern, D., & Gibbs, J. (2013). Social media as a catalyst for online deliberation? Exploring the affordances of Facebook and YouTube for political expression. Computers in Human Behavior, 29(3), 1159-1168. Han, X., Wang, J., Zhang, M., & Wang, X. (2020). Using social media to mine and analyze public opinion related to COVID-19 in China. International Journal of Environmental Research and Public Health, 17(8), 2788. Harel, T. O., Jameson, J. K., & Maoz, I. (2020). The normalization of hatred: Identity, affective polarization, and dehumanization on Facebook in the context of intractable political conflict. Social Media+ Society, 6(2), 2056305120913983. Hartigan, J. A., & Wong, M. A. (1979). Algorithm AS 136: A k-means clustering algorithm. Journal of the Royal Statistical Society. Series C (Applied Statistics), 28(1), 100-108. Hess, A. (2009). Resistance up in smoke: Analyzing the limitations of deliberation on YouTube. Critical Studies in Media Communication, 26(5), 411-434. Hübscher, M., & Walter, V. (2022). Attacks on Democracy?: A Troll-Attack on YouTube. In Antisemitism on Social Media, 74-92. Routledge. Hutchens, M. J., Silva, D. E., Hmielowski, J. D., & Cicchirillo, V. J. (2019). What’s in a username? Civility, group identification, and norms. Journal of Information Technology & Politics, 16(3), 203-218. Iyengar, S., & Westwood, S. J. (2015). Fear and loathing across party lines: New evidence on group polarization. American Journal of Political Science, 59(3), 690-707. Iyengar, S., Sood, G., & Lelkes, Y. (2012). Affect, not ideology: A social identity perspective on polarization. Public Opinion Quarterly, 76(3), 405–431. Jenkins, H. (2009). Confronting the challenges of participatory culture: Media education for the 21st century. The MIT Press. Jiang, J., Chen, E., Yan, S., Lerman, K., & Ferrara, E. (2020). Political polarization drives online conversations about COVID‐19 in the United States. Human Behavior and Emerging Technologies, 2(3), 200-211. Jost, J. T., Baldassarri, D. S., & Druckman, J. N. (2022). Cognitive–motivational mechanisms of political polarization in social-communicative contexts. Nature Reviews Psychology, 1(10), 560-576. Kassam R. (2014, Mar. 23). Belgian newspaper depicts Barack and Michelle Obama as apes ahead of president’s visit. http://www.breitbart.com/Breitbart-London/2014/03/23/belgian-newspaper-depicts-obamas-monkeys Kemp, S. (2021). DIGITAL 2021: TAIWAN. https://datareportal.com/reports/digital-2021-taiwan Kleanthous, S., & Otterbacher, J. (2019). Shaping the Reaction: Community Characteristics and Emotional Tone of Citizen Responses to Robotics Videos at TED versus YouTube. In Adjunct Publication of the 27th Conference on User Modeling, Adaptation and Personalization, 325-330. Kludt, T. (2016, Sept. 29). Harry Reid: ‘Trump is the GOP’s Frankenstein monster.’ https://edition.cnn.com/2016/09/29/politics/harry-reid-donald-trump-frankenstein-monster/index.html Kruse, L. M., Norris, D. R., & Flinchum, J. R. (2018). Social media as a public sphere? Politics on social media. The Sociological Quarterly, 59(1), 62-84. Kwon, K. H., & Gruzd, A. (2017). Is offensive commenting contagious online? Examining public vs interpersonal swearing in response to Donald Trump’s YouTube campaign videos. Internet Research, 27(4), 991-1010. Lee, C. S. (2019). Web series, YouTube, and politics: Affective and emotional dimensions of WIGS Lauren’s user comments. Social Media+ Society, 5(1), 1-12. Liebeskind, C., Liebeskind, S., & Yechezkely, S. (2021). An Analysis of Interaction and Engagement in YouTube Live Streaming Chat. In 2021 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Internet of People and Smart City Innovation, 272-279. Lindgren, S. (2010). At the Nexus of Destruction and Creation: Pirate and anti-pirate discourse in Swedish online media. In NMIC April 28-30, 2010, 229-236. Marmara University. Loader, B. D., & Mercea, D. (2011). Networking democracy? Social media innovations and participatory politics. Information, Communication & Society, 14(6), 757-769. Madden, A., Ruthven, I., & McMenemy, D. (2013). A classification scheme for content analyses of YouTube video comments. Journal of Documentation, 69, 693-714. McCoy, J., Rahman, T., & Somer, M. (2018). Polarization and the global crisis of democracy: Common patterns, dynamics, and pernicious consequences for democratic polities. American Behavioral Scientist, 62(1), 16-42. Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013). Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781. Mishra, C., & Rath, N. (2020). Social solidarity during a pandemic: Through and beyond Durkheimian Lens. Social Sciences & Humanities Open, 2(1), 100079. Northrup T. A. (1989). The dynamic of identity in personal and social conflict. In Kriesberg L., Northrup T. A., Thorson S. J. (Eds.), Intractable Conflicts and Their Transformation, 55-82. Syracuse University Press. Pacilli, M. G., Roccato, M., Pagliaro, S., & Russo, S. (2016). From political opponents to enemies? The role of perceived moral distance in the animalistic dehumanization of the political outgroup. Group Processes & Intergroup Relations, 19(3), 360-373. Papacharissi, Z. (2015). Affective publics: Sentiment, technology, and politics. Oxford University Press. Papacharissi, Z. (2016). Affective publics and structures of storytelling: Sentiment, events and mediality. Information, Communication & Society, 19, 307-324. Rauchfleisch, A., & Kaiser, J. (2020). The German far-right on YouTube: An analysis of user overlap and user comments. Journal of Broadcasting & Electronic Media, 64(3), 373-396. Reilly, K. (2016, Sept. 10). Read Hillary Clinton’s ‘basket of deplorables’ remarks about Donald Trump supporters. https://time.com/4486502/hillary-clinton-basket-of-deplorables-transcript/ Ridge, H. M. (2020). Enemy mine: negative partisanship and satisfaction with democracy. Political Behavior, 1-25. Rodriguez, P. L., & Spirling, A. (2022). Word embeddings: What works, what doesn’t, and how to tell the difference for applied research. The Journal of Politics, 84(1), 101-115. Rozado, D., & Al-Gharbi, M. (2022). Using word embeddings to probe sentiment associations of politically loaded terms in news and opinion articles from news media outlets. Journal of Computational Social Science, 5(1), 427-448. Schiermer, B. (2014). Durkheim's Concept of Mechanical Solidarity: Where Did It Go?. Durkheimian Studies, 20(1), 64-88. Shirky, C. (2011). The political power of social media: Technology, the public sphere, and political change. Foreign affairs, 28-41. Spartz, J. T., Su, L. Y. F., Griffin, R., Brossard, D., & Dunwoody, S. (2017). YouTube, social norms and perceived salience of climate change in the American mind. Environmental Communication, 11(1), 1-16. Splichal, S. (1999). Public opinion: Development and controversies in the twentieth century. London, NY: Rowman & Littlefield Publishers. Statista. (2022). Most popular social networks worldwide as of January 2022, ranked by number of monthly active users. https://www.statista.com/statistics/272014/global-social-networks-ranked-by-number-of-users/ Stromer-Galley, J. (2007). Measuring deliberation’s content: A coding scheme. Journal of Deliberative Democracy, 3(1). Syakur, M. A., Khotimah, B. K., Rochman, E. M. S., & Satoto, B. D. (2018). Integration k-means clustering method and elbow method for identification of the best customer profile cluster. IOP Conference Series: Materials Science and Engineering, 336(1), 12-17. Tajfel, H., & Turner, J. (1979). An integrative theory of intergroup conflict. In W. Austin & S. Worchel (Eds.), The Social Psychology of Intergroup Relations, 33-47. Tao, Y. (2021). Who should apologise: Expressing criticism of public figures on Chinese social media in times of COVID-19. Discourse & Society, 32(5), 622-638. Wang, C. J., Ng, C. Y., & Brook, R. H. (2020). Response to COVID-19 in Taiwan: big data analytics, new technology, and proactive testing. Jama, 323(14), 1341-1342. Way, L. C. (2015). YouTube as a site of debate through populist politics: the case of a Turkish protest pop video. Journal of Multicultural Discourses, 10(2), 180-196. Xue, J., Chen, J., Hu, R., Chen, C., Zheng, C., Su, Y., & Zhu, T. (2020). Twitter discussions and emotions about the COVID-19 pandemic: Machine learning approach. Journal of Medical Internet Research, 22(11), e20550. Yan, W., Sivakumar, G., & Xenos, M. A. (2018). It’s not cricket: examining political discussion in nonpolitical online space. Information, Communication & Society, 21(11), 1571-1587. Yousukkee, S., & Wisitpongphan, N. (2021). Analysis of spammers’ behavior on a live streaming chat. IAES International Journal of Artificial Intelligence, 10(1), 139. YouTube. (2018). Updates to YouTube Live streaming. https://blog.youtube/news-and-events/updates-to-youtube-live-streaming/ YouTube. (2020). YouTube by the numbers. https://www.youtube.com/about/press/ Zheng, C., Xue, J., Sun, Y., & Zhu, T. (2021). Public Opinions and Concerns Regarding the Canadian Prime Minister’s Daily COVID-19 Briefing: Longitudinal Study of YouTube Comments Using Machine Learning Techniques. Journal of Medical Internet Research, 23(2), e23957. | - |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/87776 | - |
| dc.description.abstract | 網路社群平台能否作為線上公共領域，讓人們不受限制地獲取資訊、不受制度影響平等地參與理性政治討論，是學界討論已久的議題：樂觀者認為，社群媒體重振了公共領域，使用者得獲得更多資訊近用權、公開發表意見的機會；悲觀者則認為，社群平台匿名性、去個體化特質，常促成攻擊性與仇恨言論，加上可能有受情緒所連結與動員的情感公眾、情緒渲染等現象，較難達到理性審議理想。其中，現今社群影音平台蔚為盛行，諸多使用者會於直播聊天室即時留言互動，然而相較其他平台，目前針對影音直播聊天室之輿論分析尚未成顯學，其參與者之行為模式與言論特徵值得進一步探索。
本研究運用文字探勘方法搭配內容分析，以新冠疫情記者會衛生福利部疾病管制署YouTube頻道直播聊天室為研究標的，旨在探索疫情記者會直播觀眾呈現的網路公眾與社群特性，以及直播聊天室作為線上審議空間之潛力與難處。此外，由留言中並觀察到相異政黨立場者爭執論戰之情形，並以政黨支持者代稱使用作為切入點，嘗試由政黨認同極化、負面黨性、群體衝突等概念詮釋解讀。 本研究發現，疫情記者會觀眾多為輕度參與、僅有少數熱衷者持續參與，曾於五場以上記者會留言者已是最積極參與的前10%用戶，亦有部分洗版者干擾討論秩序；留言內容以社交性、情緒性為多，雖有提出意見看法者但為少數；觀眾之間的回覆互動，直至疫情中後期始增加。至於直播聊天室中圍繞政黨立場的爭論，得發現自第一波本土疫情爆發後佔比上升，且在第二波本土疫情時期，執政黨受到的抨擊顯著增長。本研究之方法與技術上尚有未臻完美之處，仍可為後續從事YouTube直播聊天室分析、政治輿情分析研究者作為參考。 | zh_TW |
| dc.description.abstract | Whether online social media platforms can serve as a public sphere where individuals can access information without restrictions and participate in rational political discussions free from institutional regulation has long been debated. Optimists argue that social media revitalizes the public sphere, providing users with more information access and opportunities to express their opinions openly. Pessimists, on the other hand, believe that the anonymity and depersonalization of social media often lead to aggressive and hateful speech; besides, netizens often mobilized by emotions, makes it difficult to achieve the ideal of rational deliberation. Plenty of users engage in real-time interactions through live streaming chat rooms nowadays, however, compared to other social media platforms, the analysis of live chat rooms has not yet become a prominent field, and the behavioral patterns and speech features of participants are worthy of further exploration.
In this research, text mining methods combined with content analysis are used to explore the online public and community characteristics of the YouTube audience of the COVID-19 Press Conference held by Taiwan Centers for Disease Control (CDC). The potential and difficulties of the live chat room as an online deliberation space are investigated. Furthermore, the disputes between supporters of different political parties are evident in the live chat messages, thus the labels of political party supporters are used as a point of entry, and concepts such as political polarization, negative partisanship and group conflict are adopted to interpret and explain the phenomenon. The research has found that most of the COVID-19 Press Conference audience in the live chat rooms are lightly involved, with only a few enthusiastic participants, those who have left messages at more than five press conferences are already the top 10% of the most active users, and there are also spammers who disrupt discussion order. The content of the chat messages is mostly social and emotional, few of them propose arguments. The conversations between the audience gradually increase since the later stages of the epidemic. As for the debates surrounding political stance, it is found that the proportion of disputes has increased since the first wave of the domestic epidemic; moreover, during the second wave of the domestic epidemic, there is a significant growth in criticisms of the ruling party. The research methods and techniques still have room for improvement, but they can serve as a reference for future researchers analyzing YouTube live chat rooms and political public opinion. | en |
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| dc.description.provenance | Made available in DSpace on 2023-07-19T16:25:55Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | 誌謝 i
摘要 ii Abstract iii 目錄 v 圖目錄 vii 表目錄 viii 第一章 緒論 1 第一節 研究背景 1 第二節 研究目的 5 第二章 文獻探討 7 第一節 網路公共領域及網路社群 7 壹、網路公共領域與審議式民主 7 貳、網路公眾與社群特性 8 第二節 YouTube媒介特性及輿論研究 11 壹、YouTube媒介特性與文化 11 貳、YouTube留言文本分析 13 參、YouTube用戶行為與社群形成 14 第三節 政治對立群體相關理論 16 壹、從政治極化到負面黨性 16 貳、政治對立群體互動衝突 18 第四節 研究問題 20 第三章 研究方法 23 第一節 研究架構與流程 23 第二節 研究對象與資料 26 壹、研究對象 26 貳、留言蒐集 27 參、資料前處理 29 第三節 分析方法與技術 30 第四章 資料分析結果 34 第一節 疫情記者會直播聊天室整體觀眾參與 34 壹、疫情各時期觀眾參與情形 34 貳、記者會各階段觀眾參與情形 38 第二節 疫情記者會直播聊天室作為審議場域 40 壹、平等：個別用戶參與情況 40 貳、討論主題焦點：審議內容類型 44 參、互惠：參與者提問及回覆 47 第三節 疫情記者會直播聊天室的黨派認同極化 50 壹、由政黨支持者代稱檢視具政治立場群體 50 貳、各政治立場群體留言內容特徵 60 參、政治對立群體間的衝突論述 65 第一節 主要研究發現 70 第二節 研究限制與建議 76 參考文獻 78 壹、中文文獻 78 貳、外文文獻 79 附錄 87 壹、斷詞自定義辭典 87 貳、疫情時期與記者會階段直播留言主題類型及關鍵詞 88 參、直播觀眾標註他人留言內容類型定義與範例文本 95 | - |
| dc.language.iso | zh_TW | - |
| dc.subject | 負面黨性 | zh_TW |
| dc.subject | 政治極化 | zh_TW |
| dc.subject | 審議民主 | zh_TW |
| dc.subject | 文字探勘 | zh_TW |
| dc.subject | 輿論分析 | zh_TW |
| dc.subject | YouTube直播聊天室 | zh_TW |
| dc.subject | Negative Partisanship | en |
| dc.subject | Text Mining | en |
| dc.subject | Public Opinion | en |
| dc.subject | Political Polarization | en |
| dc.subject | YouTube Live Chat | en |
| dc.subject | Deliberative Democracy | en |
| dc.title | 社群對話中的審議民主與政治極化：以新冠疫情記者會YouTube直播聊天室為例 | zh_TW |
| dc.title | An Analysis of Deliberative Democracy and Political Polarization in Social Media Dialogue: A Case of YouTube Live Chat Messages on CDC COVID-19 Pandemic Press Conference | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 111-2 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 蔡蕙如;陳正賢 | zh_TW |
| dc.contributor.oralexamcommittee | Hui-Ju Tsai;Cheng-Hsien Chen | en |
| dc.subject.keyword | YouTube直播聊天室,輿論分析,文字探勘,審議民主,政治極化,負面黨性, | zh_TW |
| dc.subject.keyword | YouTube Live Chat,Public Opinion,Text Mining,Deliberative Democracy,Political Polarization,Negative Partisanship, | en |
| dc.relation.page | 98 | - |
| dc.identifier.doi | 10.6342/NTU202300737 | - |
| dc.rights.note | 同意授權(限校園內公開) | - |
| dc.date.accepted | 2023-04-24 | - |
| dc.contributor.author-college | 社會科學院 | - |
| dc.contributor.author-dept | 新聞研究所 | - |
| 顯示於系所單位： | 新聞研究所 | |
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