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  1. NTU Theses and Dissertations Repository
  2. 電機資訊學院
  3. 資訊工程學系
請用此 Handle URI 來引用此文件: http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/52854
完整後設資料紀錄
DC 欄位值語言
dc.contributor.advisor周承復(Cheng-Fu Chou)
dc.contributor.authorCheng-Hsuan Wuen
dc.contributor.author吳政軒zh_TW
dc.date.accessioned2021-06-15T16:30:45Z-
dc.date.available2016-08-20
dc.date.copyright2015-08-20
dc.date.issued2015
dc.date.submitted2015-08-13
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their social networks, and why?: a survey study of status message q&a behavior.
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32
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algorithms: Research methods for detecting discrimination on internet platforms.
Data and Discrimination: Converting Critical Concerns into Productive Inquiry,
2014.
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Vuong, Karrie Karahalios, Kevin Hamilton, and Christian Sandvig. I always assumed
that i wasn’t really that close to [her]”: Reasoning about invisible algorithms
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[18] R Kelly Garrett. Echo chambers online?: Politically motivated selective exposure
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[19] Eli Pariser. The filter bubble: What the Internet is hiding from you. Penguin UK,
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Power of the few vs. wisdom of the crowd: Wikipedia and the rise of the bourgeoisie.
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[21] Sabine Niederer and José Van Dijck. Wisdom of the crowd or technicity of content?
wikipedia as a sociotechnical system. New Media & Society, 12(8):1368–1387, 2010.
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engaging politics: An examination of the interactive relationships between structural
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32(1):87–111, 2005.
[23] Judd Antin and Coye Cheshire. Readers are not free-riders: reading as a form of
participation on wikipedia. In Proceedings of the 2010 ACM conference on Computer
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[24] Daniel Gruhl, Ramanathan Guha, David Liben-Nowell, and Andrew Tomkins. Information
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[27] Social Media 2013: User Demographics For Facebook, Twitter, Pinterest And
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on twitter ideologically biased? In Proceedings of the 2013 conference on Computer
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Mobile Computing and Communications Review, 5(1):3–55, 2001
dc.identifier.urihttp://tdr.lib.ntu.edu.tw/jspui/handle/123456789/52854-
dc.description.abstract隨著近年來網路的蓬勃發展,過濾機制在我們每天接觸到的許多數
位內容都扮演著極為重要的角色,例如您使用google 得到的搜尋結果
都是它根據以往的搜尋記錄來提供個人化的回應;或是在YouTube 等
許多音樂網站也都會根據您的喜愛提供推薦列表。而臉書作為知名的
社群網站,它同時具有社交以及資訊功能,人們能在認識新朋友或是
與舊朋友保持聯絡不管你們之間到底有多遠,同時也可以在動態消息
上面閱讀各式各樣的文章,而作為世界上擁有最多使用者的社群網站,
根據統計每天有數以億計的內容跟照片在上面被分享。但是存在於臉
書動態消息的過濾機制也帶來了一些社會學者的討論傳統,”過濾氣泡
現象”以及”回聲室”這兩個現象說明臉書使用者更容易因為過濾機制
的存在而更容易去接觸到與他們意識形態或價值觀相符合的內容,但
同時也有學者認為暴露在多元的言論底下不僅可以幫助人們養成民主
思維,同時也因為在每個人都有相同權利去發表不同的意見時也會帶
來所謂的”群眾智慧”。在這個研究內,我們想量化臉書動態消息的
同質性程度,同時去找到使用者的行為對同質性程度的影響力,最後
也會提出使用者動態消息的滿意程度與同質性程度之間的關係。
zh_TW
dc.description.abstractOur daily digital life is full of algorithmically filtered content such as social
media feeds, recommendations to the pages you might like, and personalized
the search results. Facebook known as the famous platform, have social
function of keeping in touch with friends in despite of distances and informational
function of reading articles of various categories from friends’posting.
However the curating algorithm may also bring some phenomenon in Facebook
News Feed. The “filter bubble”and “echo chambers”describes that
individuals more accessible to the information which their own ideology similar
to, or contents which have values consistent with. Many sociologists considered
that the opportunity to be exposed to diverse opinion have a positive
impact on developing a democratic society and bringing crowd of wisdom.
In this study, we want to quantify the homogeneity on the Facebook News
Feed and provide insight how user engagement involved with. And we would
also discuss the relation between homogeneity and user’s satisfaction.
en
dc.description.provenanceMade available in DSpace on 2021-06-15T16:30:45Z (GMT). No. of bitstreams: 1
ntu-104-R02922133-1.pdf: 1032327 bytes, checksum: 6aad5dd4f3ad356f9da0b73e12b65623 (MD5)
Previous issue date: 2015
en
dc.description.tableofcontents口試委員會審定書i
致謝ii
中文摘要iii
Abstract iv
Contents v
List of Figures vii
List of Tables ix
1 Introduction 1
2 Related Works 6
3 Study Design 8
3.1 Experiment Flow 8
3.2 Coding the posts 9
3.3 Questionnaire 12
4 Measures 15
4.1 Participants 15
4.2 Homogeneity Measures 16
4.3 Satisfaction Measures 18
5 Results and Analysis 20
5.1 Potential Reasons about Homogeneity 21
5.2 Relationship between Satisfaction and Homogeneity 23
6 Conclusion 30
Bibliography 32
dc.language.isoen
dc.subject同質性程度zh_TW
dc.subject動態消息zh_TW
dc.subject臉書zh_TW
dc.subjectfacebooken
dc.subjectNews Feeden
dc.subjectHomogeneityen
dc.title探討臉書動態消息的同質性程度及相關的影響因素zh_TW
dc.titleFacebook News Feed Homogeneity and Associated Factorsen
dc.typeThesis
dc.date.schoolyear103-2
dc.description.degree碩士
dc.contributor.coadvisor畢恆達(Herng-Dar Bih)
dc.contributor.oralexamcommittee吳曉光(Hsiao-kuang Wu),黃心怡(Hsin-I Huang),彭立沛(Li-Pei Peng)
dc.subject.keyword臉書,動態消息,同質性程度,zh_TW
dc.subject.keywordfacebook,News Feed,Homogeneity,en
dc.relation.page35
dc.rights.note有償授權
dc.date.accepted2015-08-13
dc.contributor.author-college電機資訊學院zh_TW
dc.contributor.author-dept資訊工程學研究所zh_TW
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