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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 江淳芳(Chun-Fang Chiang) | |
| dc.contributor.author | Fu-Hsuan Lin | en |
| dc.contributor.author | 林甫軒 | zh_TW |
| dc.date.accessioned | 2022-11-24T03:36:50Z | - |
| dc.date.available | 2021-09-02 | |
| dc.date.available | 2022-11-24T03:36:50Z | - |
| dc.date.copyright | 2021-09-02 | |
| dc.date.issued | 2021 | |
| dc.date.submitted | 2021-07-30 | |
| dc.identifier.citation | Agichtein, Eugene, Eric Brill, and Susan Dumais. (2006). “Improving Web Search Ranking by Incorporating User Behavior Information.” Proceedings of the 29th Annual International ACM Sigir Conference on Research and Development in Information Retrieval, 19–26. ACM. Alemán, E., Calvo, E., Jones, M. P., Kaplan, N. (2009). Comparing Cosponsorship and Roll‐Call Ideal Points. Legislative Studies Quarterly, 34(1), 87-116. Bafumi, J., Herron, M. C. (2010). Leapfrog representation and extremism: A study of American voters and their members in Congress. American Political Science Review, 519-542. Bailey, M. A. (2007). Comparable preference estimates across time and institutions for the court, congress, and presidency. American Journal of Political Science, 51(3), 433-448. Benkler, Yochai. (2006). The Wealth of Networks: How Social Production Transforms Markets and Freedom. New Haven, CT: Yale University Press Bond, R., Messing, S. (2015). Quantifying social media’s political space: Estimating ideology from publicly revealed preferences on Facebook. American Political Science Review, 109(1), 62-78. Bonica, A. (2013). Ideology and interests in the political marketplace. American Journal of Political Science, 57(2), 294-311. Carmines, E. G., Ensley, M. J., Wagner, M. W. (2012). Who fits the left-right divide? Partisan polarization in the American electorate. American Behavioral Scientist, 56(12), 1631-1653. Carpini, M. X. D. (2004). Mediating democratic engagement: The impact of communications on citizens' involvement in political and civic life. Feldman, S. (1988). Structure and consistency in public opinion: The role of core beliefs and values. American Journal of political science, 416-440. Freeden, M. (2006). Ideology and political theory. Journal of Political Ideologies, 11(1), 3-22. Friedkin, N. E., Johnsen, E. C. (2011). Social influence network theory: A sociological examination of small group dynamics (Vol. 33). Cambridge University Press. Gentzkow, M., Shapiro, J. M. (2010). What drives media slant? Evidence from US daily newspapers. Econometrica, 78(1), 35-71. Gerber, E. R., Lewis, J. B. (2004). Beyond the median: Voter preferences, district heterogeneity, and political representation. Journal of Political Economy, 112(6), 1364-1383. Goren, Paul. (2005). Party Identification and Core Political Values. American Journal of Political Science 49: 881–96. Groseclose, T., Milyo, J. (2005). A measure of media bias. The Quarterly Journal of Economics, 120(4), 1191-1237. Hoff, P. D., Raftery, A. E., Handcock, M. S. (2002). Latent space approaches to social network analysis. Journal of the american Statistical association, 97(460), 1090-1098. Klar, S. (2014). A multidimensional study of ideological preferences and priorities among the American public. Public Opinion Quarterly, 78(S1), 344-359. Laver, M., Benoit, K., Garry, J. (2003). Extracting policy positions from political texts using words as data. American political science review, 311-331. Monroe, B. L., Maeda, K. (2004). Rhetorical ideal point estimation: Mapping legislative speech. Society for Political Methodology, Stanford University. Monroe, B. L., Colaresi, M. P., Quinn, K. M. (2008). Fightin'words: Lexical feature selection and evaluation for identifying the content of political conflict. Political Analysis, 16(4), 372-403. Munson, Sean A., and Paul Resnick. (2010). “Presenting Diverse Political Opinions: How and How Much.” Proceedings of the Sigchi Conference on Human Factors in Computing Systems, 1457–1466. ACM. Pew Research Center. (2014). 'Political Polarization and Media Habits.' Pew Research Center. (2016a). 'News Use Across Social Media Platforms 2016.' Pew Research Center. (2016b). 'Social Media Update 2016.' Pew Research Center. (2016c). 'The Political Environment on Social Media.' Poole, Keith T., and Howard Rosenthal. (1997). Congress: A Political-Economic History of Roll Call Voting. New York : Oxford University Press. Putnam, R. D. (1995). Tuning in, tuning out: The strange disappearance of social capital in America. PS: Political science politics, 28(4), 664-684. Raftery, A. E., Niu, X., Hoff, P. D., Yeung, K. Y. (2012). Fast inference for the latent space network model using a case-control approximate likelihood. Journal of Computational and Graphical Statistics, 21(4), 901-919. Schickler, E., Lee, F. E., Edwards III, G. C. (Eds.). (2011). The Oxford Handbook of the American Congress. Oxford University Press. Treier, S., Hillygus, D. S. (2009). The nature of political ideology in the contemporary electorate. Public Opinion Quarterly, 73(4), 679-703. Wood, T., Oliver, E. (2012). Toward a more reliable implementation of ideology in measures of public opinion. Public Opinion Quarterly, 76(4), 636-662. Wright, Gerald C., Robert S. Erikson, and John P. McIver. (1985). Measuring State Partisanship and Ideology with Survey Data. Journal of Politics 47: 469–89 | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/81218 | - |
| dc.description.abstract | 本文利用人們在Facebook上的政治參與，估計使用者的意識型態分布，聚焦在2016美國總統大選中，Facebook的使用者與粉專的互動行為，使用社會網絡分析的動態潛空間模型(Latent space approaches)進行估計。該方法不僅可以估計粉專的意識型態位置，還可以估計使用者的意識形態，這使得本文可以查看每個粉專背後的使用者意識型態分布。 本文估計出來的意識型態分布，可以很好地將支持兩邊候選人的粉專給區分出來，對於各政黨、各家媒體的意識型態估計也都與人們普遍的認知相符，而使用者的意識形態也呈現雙峰的分布。估計的結果也與DW-Nominate方法、PCA方法得出的意識型態估計結果有高度相關性。 | zh_TW |
| dc.description.provenance | Made available in DSpace on 2022-11-24T03:36:50Z (GMT). No. of bitstreams: 1 U0001-2907202122385300.pdf: 2349160 bytes, checksum: 26edf5c61cd6ca33a2bf3d2ece2f9749 (MD5) Previous issue date: 2021 | en |
| dc.description.tableofcontents | 壹、前言 1 貳、文獻回顧 3 參、研究方法 9 一、 數據集 9 二、 臉書按讚資料的動態潛空間模型 11 (一)、節點間形成連結 12 (二)、形成連結的可能性 12 (三)、網絡的形成 13 (四)、模型的迭代 13 (五)、Case-Control 方法 14 三、 數據與模型 15 肆、模型結果：第一、二種樣本：知名粉專、政治人物 18 一、 模型參數設定 18 二、 模型結果初探 18 (一)、 第一種樣本：模型MCMC迭代紀錄 18 (二)、 第一種樣本：粉專潛變量與粉專連結數分布 19 (三)、 第一種樣本：所有粉專潛變量分布 20 (四)、 候選人相關粉專 20 (五)、 政治人物相關粉專 22 (六)、 全國性媒體粉專 23 (七)、 使用者 24 (八)、 選前四周粉專潛變量 24 三、 外部數據集驗證 26 (一)、 PCA第一維 26 (二)、 DW-Nominate方法第一維 27 伍、模型結果：第三種樣本：真假新聞貼文 28 一、 模型樣本概況 28 二、 模型參數設定 28 三、 模型結果初探 28 (一) 五大陣營粉專貼文 29 四、 與第一種樣本驗證 29 陸、應用與討論 31 一、 中立選民 31 二、 假新聞 36 柒、結論 40 捌、參考文獻 43 | |
| dc.language.iso | zh-TW | |
| dc.subject | 社群媒體 | zh_TW |
| dc.subject | 臉書 | zh_TW |
| dc.subject | 政治參與 | zh_TW |
| dc.subject | 潛空間模型 | zh_TW |
| dc.subject | 意識型態估計 | zh_TW |
| dc.subject | 社會網路 | zh_TW |
| dc.subject | social network | en |
| dc.subject | latent space approach | en |
| dc.subject | en | |
| dc.subject | ideology estimation | en |
| dc.subject | political participation | en |
| dc.subject | social media | en |
| dc.title | 以社會網絡觀點估計FB使用者意識型態分布 | zh_TW |
| dc.title | Estimating the ideological distribution on Facebook by the approach of social network | en |
| dc.date.schoolyear | 109-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 謝志昇(Hsin-Tsai Liu),楊子霆(Chih-Yang Tseng),張佑宗 | |
| dc.subject.keyword | 意識型態估計,政治參與,社群媒體,社會網路,潛空間模型,臉書, | zh_TW |
| dc.subject.keyword | latent space approach,Facebook,ideology estimation,political participation,social media,social network, | en |
| dc.relation.page | 46 | |
| dc.identifier.doi | 10.6342/NTU202101915 | |
| dc.rights.note | 同意授權(限校園內公開) | |
| dc.date.accepted | 2021-08-02 | |
| dc.contributor.author-college | 社會科學院 | zh_TW |
| dc.contributor.author-dept | 經濟學研究所 | zh_TW |
| 顯示於系所單位： | 經濟學系 | |
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