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題名 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
作者 曾子朋
Tseng, Zi-Peng
貢獻者 周珮婷<br>陳怡如
曾子朋
Tseng, Zi-Peng
關鍵詞 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
日期 2023
上傳時間 2-Aug-2023 13:04:51 (UTC+8)
摘要 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.
參考文獻 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.
描述 碩士
國立政治大學
統計學系
110354020
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0110354020
資料類型 thesis
dc.contributor.advisor 周珮婷<br>陳怡如zh_TW
dc.contributor.author (Authors) 曾子朋zh_TW
dc.contributor.author (Authors) Tseng, Zi-Pengen_US
dc.creator (作者) 曾子朋zh_TW
dc.creator (作者) Tseng, Zi-Pengen_US
dc.date (日期) 2023en_US
dc.date.accessioned 2-Aug-2023 13:04:51 (UTC+8)-
dc.date.available 2-Aug-2023 13:04:51 (UTC+8)-
dc.date.issued (上傳時間) 2-Aug-2023 13:04:51 (UTC+8)-
dc.identifier (Other Identifiers) G0110354020en_US
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/146309-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 統計學系zh_TW
dc.description (描述) 110354020zh_TW
dc.description.abstract (摘要) 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。zh_TW
dc.description.abstract (摘要) 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.en_US
dc.description.tableofcontents 摘要 i
Abstract ii
目錄 iv
圖目錄 vi
表目錄 vii
第一章 緒論 1
第一節 研究背景與動機 1
第二節 研究問題與目的 2
第二章 文獻探討 3
第一節 YouTuber 間合作 3
第二節 社會網路分析 6
第三章 研究方法 9
第一節 資料收集與處理 9
第二節 社會網路分析 13
第四章 YouTuber 合作網絡分析 22
第一節 社會網路分析 22
第二節 Leiden 演算法的結果分析 26
第三節 ERGM 結果分析 29
第五章 結論與建議 34
第一節 結論 34
第二節 建議與限制 35
參考文獻 42		zh_TW
dc.format.extent 1775233 bytes-
dc.format.mimetype application/pdf-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0110354020en_US
dc.subject (關鍵詞) YouTuberzh_TW
dc.subject (關鍵詞) 影片合作zh_TW
dc.subject (關鍵詞) 社會網路分析zh_TW
dc.subject (關鍵詞) 社群偵測zh_TW
dc.subject (關鍵詞) 指數隨機圖模型zh_TW
dc.subject (關鍵詞) YouTuberen_US
dc.subject (關鍵詞) Video Collaborationen_US
dc.subject (關鍵詞) Social Network Analysisen_US
dc.subject (關鍵詞) Community Detectionen_US
dc.subject (關鍵詞) Exponential Random Graph Modelsen_US
dc.title (題名) 利用指數隨機圖模型分析 YouTuber 間影片合作因素zh_TW
dc.title (題名) Analyzing Factors of Collaboration among YouTubers using Exponential Random Graph Modelsen_US
dc.type (資料類型) thesisen_US
dc.relation.reference (參考文獻) 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.		zh_TW