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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 謝志昇 | zh_TW |
| dc.contributor.advisor | Chih-Sheng Hsieh | en |
| dc.contributor.author | 崔丞皓 | zh_TW |
| dc.contributor.author | Cheng-Hao Tsuie | en |
| dc.date.accessioned | 2025-08-18T16:19:13Z | - |
| dc.date.available | 2025-08-19 | - |
| dc.date.copyright | 2025-08-18 | - |
| dc.date.issued | 2025 | - |
| dc.date.submitted | 2025-08-08 | - |
| dc.identifier.citation | Allcott, Hunt and Matthew Gentzkow (2017) “Social Media and Fake News in the 2016 Election,” Journal of Economic Perspectives, 31 (2), 211–236, 10.1257/jep.31.2.211.
Anselin, Luc (1988) Spatial econometrics: Methods and models, Dordrecht: Kluwer Academic Publishers. Blondel, Vincent D., Jean-Loup Guillaume, Renaud Lambiotte, and Etienne Lefebvre (2008) “Fast unfolding of communities in large networks,” Journal of Statistical Mechanics: Theory and Experiment, 2008 (10), P10008. Cinelli, Matteo, Gianmarco De Francisci Morales, Alessandro Galeazzi, Walter Quattrociocchi, and Michele Starnini (2021) “The Echo Chamber Effect on Social Media,”Proceedings of the National Academy of Sciences, 118 (9), e2023301118. Faust, Katherine (1997) “Centrality in affiliation networks,” Social Networks, 19 (2), 157–191, 10.1016/S0378-8733(96)00300-0. Fujimoto, Kayo, Chih-Ping Chou, and Thomas W. Valente (2011) “The network autocorrelation model using two-mode data: Affiliation exposure and potential bias in the autocorrelation parameter,” Social Networks, 33 (3), 231–243. Guess, Andrew, Jonathan Nagler, and Joshua Tucker (2019) “Less than you think: Prevalence and Predictors of Fake News Dissemination on Facebook,” Science Advances, 5(1), eaau4586. Guess, Andrew, Brendan Nyhan, and Jason Reifler (2020) “Exposure to Untrustworthy Websites in the 2016 US Election,” Nature Human Behaviour, 4, 472–480. Lazer, David M. J., Matthew A. Baum, Yochai Benkler et al. (2018) “The Science of Fake News,” Science, 359 (6380), 1094–1096. Lenti, Mauro, Elena Pavan, Stefania Martini, and Giulio Rossetti (2023) “Global Misinformation Spillovers in the Vaccination Debate Before and During the COVID-19 Pandemic: Multilingual Twitter Study,” Journal of Medical Internet Research,25,e44043. Lewandowsky, Stephan, Ullrich K. H. Ecker, and John Cook (2017) “Beyond Misinformation: Understanding and Coping with the ”Post-Truth” Era,” Journal of Applied Research in Memory and Cognition, 6 (4), 353–369. Pew Research Center (2016) “Social Media Update 2016,” https://www.pewresearch.org. Vosoughi, Soroush, Deb Roy, and Sinan Aral (2018) “The Spread of True and False News Online,” Science, 359 (6380), 1146–1151. | - |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/98744 | - |
| dc.description.abstract | 本研究以 2016 年美國總統大選期間 Facebook 粉絲專頁之假新聞散播現象為觀察對象,探討粉專間是否存在假新聞發布行為的跨粉專外溢效應,並深入分析其背後的傳播機制與社群結構特性。本研究透過社會網絡分析與空間自迴歸(SAR)模型分析,利用 941 個 Facebook 粉絲專頁自 2016 年 7 月 31 日至 11 月 5日之縱橫資料,建構使用者與粉專間的二模網絡並進一步轉換為粉專間的一模網絡,並以特徵向量中心性分析粉專的重要性及互動結構特性。
研究結果顯示,假新聞的發布高度集中於少數特定粉專,且呈現明顯的政治不對稱性,Pro-Trump 陣營粉專的假新聞發布數量顯著高於 Pro-Clinton 陣營。此外,粉專之間透過高度共享的使用者形成明確且穩定的社群結構,呈現出顯著的政治分化特徵,即所謂的「同溫層效應」。中心性分析更進一步揭示,Pro-Trump陣營內部的互動結構較為密切與持續,Pro-Clinton 陣營則更傾向於短期且高度依賴外部政治事件的刺激。SAR 模型分析進一步證實,粉專發布假新聞的行為明顯具有跨粉專的模仿效應,特別是當同一政治陣營內的其他粉專增加假新聞發布頻率後,粉專本身也傾向跟進模仿此行為,進一步增加自身的假新聞發布數量。其中 Pro-Trump 陣營的外溢效果最強烈,而跨陣營間的外溢效應則相對較弱。此外,本研究發現假新聞的外溢傳播效果也受到粉專自身性質的影響,尤其是媒體型與組織型粉專具有較強的外溢性質。 | zh_TW |
| dc.description.abstract | This study examines the dissemination of fake news among Facebook fan pages during the 2016 U.S. Presidential Election, specifically investigating whether fake news posting behaviors exhibit cross-page spillover effects, and further analyzing the underlying dissemination mechanisms and community structures. Using social network analysis and Spatial Autoregressive (SAR) modeling, this research utilizes panel data comprising 941 Facebook fan pages from July 31 to November 5, 2016. The analysis begins by constructing a two-mode network representing interactions between users and fan pages, subsequently transforming it into a one-mode network that captures interactions among fanpages. Eigenvector centrality is then applied to evaluate the significance and interaction characteristics of these fan pages within the network.
The findings reveal that fake news postings are highly concentrated among a small number of fan pages, displaying clear political asymmetry, with Pro-Trump pages posting significantly more fake news compared to Pro-Clinton pages. Additionally, fan pages form distinct and stable community structures through highly shared users, characterized by significant political polarization, often referred to as an ”echo chamber” effect. Centrality analysis further indicates that the internal interaction structures within Pro-Trump pages are tighter and more sustained, while those within Pro-Clinton pages tend to be short-term and heavily reliant on external political events. Moreover, the SAR modeling analysis confirms significant cross-page spillover effects in fake news posting behaviors, especially prominent within the same political camp,with the strongest spillover effect observed among Pro-Trump fan pages, whereas cross-camp spillover effects are comparatively weaker. This study also finds that the nature of fan pages influences the strength of spillover effects, with media-type and organization-type fan pages exhibiting stronger spillover properties. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2025-08-18T16:19:13Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2025-08-18T16:19:13Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | Acceptance Certificate i
摘要 ii Abstract iv 目次 vi 圖次 viii 表次 ix 第一章 前言 1 第二章 文獻回顧 4 第三章 資料與方法 8 3.1 資料選取 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 3.2 矩陣建構與分析方法 . . . . . . . . . . . . . . . . . . . . . . . . . . 10 第四章 結果 15 4.1 假新聞整體發布趨勢 . . . . . . . . . . . . . . . . . . . . . . . . . . 15 4.2 不同陣營粉專之描述性統計與差異檢定 . . . . . . . . . . . . . . . . 18 4.3 社群偵測 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 4.4 中心性分析 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 4.5 社群結構對於粉專發布假新聞行為之外溢效應 . . . . . . . . . . . . 32 4.6 外溢效應分析 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 第五章 結論 41 參考文獻 44 附錄 A — 度中心性與特徵向量中心性 46 A.1 度中心性分析 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46 | - |
| dc.language.iso | zh_TW | - |
| dc.subject | zh_TW | |
| dc.subject | 假新聞 | zh_TW |
| dc.subject | 外溢效應 | zh_TW |
| dc.subject | 空間自迴歸模型 | zh_TW |
| dc.subject | 社會網絡 | zh_TW |
| dc.subject | Social Network | en |
| dc.subject | Spatial Autoregressive Model | en |
| dc.subject | Spillover Effect | en |
| dc.subject | en | |
| dc.subject | Fake news | en |
| dc.title | Facebook 粉絲專頁發布假新聞行為之網路外溢效果分析:以 2016 年美國總統大選為例 | zh_TW |
| dc.title | Dissemination of Fake News Among Facebook Fan Pages:A Study of the 2016 U.S. Presidential Election | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 113-2 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 江淳芳;謝吉隆 | zh_TW |
| dc.contributor.oralexamcommittee | Chun-Fang Chiang;Ji-Lung Hsieh | en |
| dc.subject.keyword | 假新聞,Facebook,社會網絡,空間自迴歸模型,外溢效應, | zh_TW |
| dc.subject.keyword | Fake news,Facebook,Social Network,Spatial Autoregressive Model,Spillover Effect, | en |
| dc.relation.page | 48 | - |
| dc.identifier.doi | 10.6342/NTU202502023 | - |
| dc.rights.note | 同意授權(限校園內公開) | - |
| dc.date.accepted | 2025-08-12 | - |
| dc.contributor.author-college | 社會科學院 | - |
| dc.contributor.author-dept | 經濟學系 | - |
| dc.date.embargo-lift | 2030-08-04 | - |
| 顯示於系所單位: | 經濟學系 | |
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