政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/146309
English  |  正體中文  |  简体中文  |  Post-Print筆數 : 27 |  Items with full text/Total items : 112871/143842 (78%)
Visitors : 49950211      Online Users : 589
RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
    •  Adv. Search
    Home ‧ Login ‧ Upload ‧ Help ‧ About ‧ Administer Goto mobile version


    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/146309


    Title: 利用指數隨機圖模型分析 YouTuber 間影片合作因素
    Analyzing Factors of Collaboration among YouTubers using Exponential Random Graph Models
    Authors: 曾子朋
    Tseng, Zi-Peng
    Contributors: 周珮婷
    陳怡如
    曾子朋
    Tseng, Zi-Peng
    Keywords: YouTuber
    影片合作
    社會網路分析
    社群偵測
    指數隨機圖模型
    YouTuber
    Video Collaboration
    Social Network Analysis
    Community Detection
    Exponential Random Graph Models
    Date: 2023
    Issue Date: 2023-08-02 13:04:51 (UTC+8)
    Abstract: 社交媒體平台已成為現代社會中最受歡迎的溝通工具之一，改變了人們的交流方式。YouTuber作為社交媒體平台上具有影響力的個人，其在內容創作和品牌合作方面扮演著重要角色。然而，對YouTuber之間的影片合作關係尚未得到充分關注。本研究旨在探討YouTuber之間的影片合作關係及其影響因素。通過社會網路分析和ERGM模型，研究了百大YouTuber的合作網絡結構和關係性質。結果顯示，高訂閱數的YouTuber更傾向主動合作，低訂閱數的YouTuber則常作為合作對象。同質性、自身特徵、傳遞性和互惠性等因素也對合作關係產生影響。這些研究結果有助於深入了解YouTuber合作關係的本質，並提供對社交媒體平台中的人際關係和信息傳播變化趨勢的理解。同時，這些結果也能夠為YouTuber、品牌合作和內容創作等方面的制定更有效的策略提供指導。
    Social media platforms have become one of the most popular communication tools in modern society, transforming the way people interact. YouTubers, as influential individuals on social media platforms, play a crucial role in content creation and brand collaborations. However, the interrelationships and factors influencing video collaborations among YouTubers have received limited attention. This study aims to explore the collaborative relationships and influencing factors among YouTubers. By employing social network analysis and ERGM models, the collaboration networks and relationship characteristics of the top 100 YouTubers were investigated. The results reveal that YouTubers with a high number of subscribers are more likely to initiate collaborations, while those with a lower number of subscribers are often chosen as collaboration partners. Factors such as homophily, individual characteristics, transitivity, and reciprocity also influence the collaborative relationships. These findings contribute to a deeper understanding of the nature of YouTuber collaborations and provide insights into the changing trends of interpersonal relationships and information dissemination on social media platforms. Moreover, these results can guide the development of more effective strategies for YouTubers, brand collaborations, and content creation.
    Reference: Anuar, S. H. H., Abas, Z. A., Yunos, N. M., Zaki, N. H. M., Hashim, N. A., Mokhtar, M. F., Asmai, S. A., Abidin, Z. Z., and Nizam, A. F. (2021). Comparison between louvain and leiden algorithm for network structure: A review. In Journal of Physics: Conference Series, volume 2129, page 012028. IOP Publishing.
    Baatarjav, E.-A. and Dantu, R. (2011). Current and future trends in social media. pages 1384–1385.
    Blau, P. M. (1977). A macrosociological theory of social structure. American journal of sociology, 83(1):26–54.
    Blondel, V. D., Guillaume, J.-L., Lambiotte, R., and Lefebvre, E. (2008). Fast unfolding of communities in large networks. Journal of statistical mechanics: theory and experiment, 2008(10):P10008.
    Borgatti, S. P., Mehra, A., Brass, D. J., and Labianca, G. (2009). Network analysis in the social sciences. science, 323(5916):892–895.
    Burt, R. S. (2000). The network structure of social capital. Research in organizational behavior, 22:345–423.
    Byshkin, M., Stivala, A., Mira, A., Krause, R., Robins, G., and Lomi, A. (2016). Auxiliary parameter mcmc for exponential random graph models. Journal of Statistical Physics, 165:740–754.
    Centola, D. and Macy, M. (2007). Complex contagions and the weakness of long ties. American journal of Sociology, 113(3):702–734.
    Chau, C. (2010). Youtube as a participatory culture. New directions for youth development, 2010(128):65–74.
    Coates, A. E., Hardman, C. A., Halford, J. C., Christiansen, P., and Boyland, E. J. (2019). Food and beverage cues featured in youtube videos of social media influencers popular with children: an exploratory study. Frontiers in Psychology, 10:2142.
    Feld, S. L. (1991). Why your friends have more friends than you do. American journal of sociology, 96(6):1464–1477.
    Frank, O. and Strauss, D. (1986). Markov graphs. Journal of the american Statistical association, 81(395):832–842.
    Girvan, M. and Newman, M. E. (2002). Community structure in social and biological networks. Proceedings of the national academy of sciences, 99(12):7821–7826.
    Granovetter, M. S. (1973). The strength of weak ties. American journal of sociology, 78(6):1360–1380.
    Gruzd, A. and Hodson, J. (2021). Making sweet music together: The affordances of networked media for building performance capital by youtube musicians. Social Media+ Society, 7(2):20563051211025511.
    Gutiérrez-Moya, E., Lozano, S., and Adenso-Díaz, B. (2020). Analysing the structure of the global wheat trade network: an ergm approach. Agronomy, 10(12):1967.
    Krivitsky, P. N. and Handcock, M. S. (2014). A separable model for dynamic networks. Journal of the Royal Statistical Society. Series B, Statistical Methodology, 76(1):29.
    Laumann, E. O. and Guttman, L. (1966). The relative associational contiguity of occupations in an urban setting. American sociological review, pages 169–178.
    Lazarsfeld, P. F., Merton, R. K., et al. (1954). Friendship as a social process: A substantive and methodological analysis. Freedom and control in modern society, 18(1):18–66.
    Leskovec, J., Kleinberg, J., and Faloutsos, C. (2007). Graph evolution: Densification and shrinking diameters. ACM transactions on Knowledge Discovery from Data (TKDD), 1(1):2–es.
    Luscombe, B. (2015). You tube’s view master. Time, 186(9/10):70–75.
    Melendres, M. (2019). Youtubers influence of young people.
    Moody, E. J. (2001). Internet use and its relationship to loneliness. CyberPsychology & Behavior, 4(3):393–401.
    Newman, M. E. and Girvan, M. (2004). Finding and evaluating community structure in networks. Physical review E, 69(2):026113.
    Pane, M. M. and Rumeser, J. A. A. (2021). The quality of cohesiveness in collaboration between two youtube channels in delivering humour (case study: The collaboration between two particular youtube channels in indonesia). In BDET 2021: The 3rd International Conference on Big Data Engineering and Technology, Singapore, June 25-27, 2021, pages 67–73. ACM.
    Pasquel-López, C., Rodríguez-Aceves, L., and Valerio-Ureña, G. (2022). Social network analysis of edutubers. In Frontiers in Education, volume 7, page 845647. Frontiers Media SA.
    Pires, K. and Simon, G. (2015). Youtube live and twitch: a tour of user-generated live streaming systems. In Proceedings of the 6th ACM multimedia systems conference, pages 225–230.
    Rao, A. R. and Bandyopadhyay, S. (1987). Measures of reciprocity in a social network. Sankhyā: The Indian Journal of Statistics, Series A, pages 141–188.
    Rieder, B., Coromina, Ò., and Matamoros-Fernández, A. (2020). Mapping youtube: A quantitative exploration of a platformed media system. First Monday.
    Roose, K. (2019). The making of a youtube radical. The New York Times, 8.
    Rosvall, M. and Bergstrom, C. T. (2008). Maps of random walks on complex networks reveal community structure. Proceedings of the national academy of sciences, 105(4):1118–1123.
    Sharifnia, S. G. and Saghaei, A. (2022). A statistical approach for social network change detection: an ergm based framework. Communications in Statistics-Theory and Methods, 51(7):2259–2280.
    Siraj, H., Machavarapu, A., Hwang, J., Radhakrishnan, K., Adams, S., Kim, J., and Lee, M. (2023a). How do youtubers collaborate? a preliminary analysis of youtubers’collaboration networks. iConference 2023 Proceedings.
    Siraj, H., Machavarapu, A., Hwang

    , J., Radhakrishnan, K., Adams, S., Kim, J., and Lee, M. (2023b). How do youtubers collaborate? a preliminary analysis of youtubers’collaboration networks. iConference 2023 Proceedings.
    Snijders, T. A. et al. (2002). Markov chain monte carlo estimation of exponential random graph models. Journal of Social Structure, 3(2):1–40.
    Snijders, T. A., Pattison, P. E., Robins, G. L., and Handcock, M. S. (2006a). New specifications for exponential random graph models. Sociological methodology, 36(1):99–153.
    Snijders, T. A., Pattison, P. E., Robins, G. L., and Handcock, M. S. (2006b). New specifications for exponential random graph models. Sociological methodology, 36(1):99–153.
    Stivala, A. and Lomi, A. (2021). Testing biological network motif significance with exponential random graph models. Applied Network Science, 6(1):1–27.
    Traag, V. A., Waltman, L., and Van Eck, N. J. (2019). From louvain to leiden: guaranteeing well-connected communities. Scientific reports, 9(1):5233.
    Ugander, J., Karrer, B., Backstrom, L., and Marlow, C. (2011). The anatomy of the facebook social graph. arXiv preprint arXiv:1111.4503.
    Verbrugge, L. M. (1977). The structure of adult friendship choices. Social forces, 56(2):576–597.
    Wasserman, S. and Faust, K. (1994). Social network analysis: Methods and applications.
    Wu, K. (2016). Youtube marketing: Legality of sponsorship and endorsements in advertising. JL Bus. & Ethics, 22:59.
    Yoo, E., Gu, B., and Rabinovich, E. (2019). Competition and coopetition among social media content.
    Zhu, Y.-X., Zhang, X.-G., Sun, G.-Q., Tang, M., Zhou, T., and Zhang, Z.-K. (2014). Influence of reciprocal links in social networks. PloS one, 9(7):e103007.
    Description: 碩士
    國立政治大學
    統計學系
    110354020
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0110354020
    Data Type: thesis
    Appears in Collections:[Department of Statistics] Theses

    Files in This Item:

    File Description SizeFormat
    402001.pdf1733KbAdobe PDF20View/Open


    All items in 政大典藏 are protected by copyright, with all rights reserved.


    社群 sharing

    著作權政策宣告 Copyright Announcement
    1.本網站之數位內容為國立政治大學所收錄之機構典藏，無償提供學術研究與公眾教育等公益性使用，惟仍請適度，合理使用本網站之內容，以尊重著作權人之權益。商業上之利用，則請先取得著作權人之授權。
    The digital content of this website is part of National Chengchi University Institutional Repository. It provides free access to academic research and public education for non-commercial use. Please utilize it in a proper and reasonable manner and respect the rights of copyright owners. For commercial use, please obtain authorization from the copyright owner in advance.

    2.本網站之製作，已盡力防止侵害著作權人之權益，如仍發現本網站之數位內容有侵害著作權人權益情事者，請權利人通知本網站維護人員(nccur@nccu.edu.tw)，維護人員將立即採取移除該數位著作等補救措施。
    NCCU Institutional Repository is made to protect the interests of copyright owners. If you believe that any material on the website infringes copyright, please contact our staff(nccur@nccu.edu.tw). We will remove the work from the repository and investigate your claim.
    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - Feedback