Responsive image
博碩士論文 etd-0721118-133457 詳細資訊
Title page for etd-0721118-133457
論文名稱
Title
直播之觀看行為影響因素分析:以Twitch為例
Analyses of Factors that Affect Viewing Behaviors of Live Streaming: A Case Study of Twitch
系所名稱
Department
畢業學年期
Year, semester
語文別
Language
學位類別
Degree
頁數
Number of pages
93
研究生
Author
指導教授
Advisor
召集委員
Convenor
口試委員
Advisory Committee
口試日期
Date of Exam
2018-07-27
繳交日期
Date of Submission
2018-08-28
關鍵字
Keywords
直播、交互作用、Twitch、資料探勘、iRF
iRF, data mining, live streaming, Twitch, interaction
統計
Statistics
本論文已被瀏覽 6052 次,被下載 154
The thesis/dissertation has been browsed 6052 times, has been downloaded 154 times.
中文摘要
過去幾年,使用者生成內容的直播串流服務引起了大眾的興趣,各式各樣的網路直播平台也隨之興起,加上行動裝置普及與網路通訊發達,直播技術門檻降低,在人人都能夠成為直播主的時代,如何能夠吸引民眾觀看便成為了直播主的一大目標。因此本研究旨在探討影響觀看的因素,進而可以提供給直播主在製作內容時參考的方向。
  本研究以Twitch作為研究目標,利用不同學者分別於2014年及2015年所蒐集的資料集,從龐大的直播資料以資料探勘方法找出影響觀看人數的重要因素。透過從iterative Random Forests (iRF) 得出的交互作用之穩定性,辨別影響觀看人數的變數之交互作用。由於iRF的交互作用的結果僅得知有影響的變數和等同於統計顯著的穩定分數,因此會接續iRF的結果以線性迴歸觀察各個顯著的變數以及利用決策樹觀察較容易被分類為觀看人數較多的分支依據。兩份資料集中的頻道總觀看次數在線性迴歸分析結果中呈現非常顯著,在決策樹的分支依據中,則是分別列出容易獲得較多觀看人數的部分時區和部分遊戲,能夠提供給直播主作為參考。除了從既定的資料集當中尋找影響觀看的因素,也對影響觀看的當地社會文化進行觀察,而以上的分析結果以及對顯著變數背後的社會環境和文化推廣等觀察,能夠利用計畫行為理論來綜合整理研究結果,表明研究分析的結果會對觀眾的觀看意圖產生影響。
  另外基於不同國家有不同的背景與市場,因此也探討在不同的國家之下對於不同類型直播的熱衷程度與喜好,對於剛從事直播領域或想創造與提升自身觀眾群的直播主而言,觀看人數的中位數是較容易達成的目標,因此在探討直播類型時也會以觀看的中位數作為指標,讓直播主也能夠了解觀眾的喜好,提供給不同國家的直播主可以針對目標國家觀眾喜愛的類型來設計直播內容。
Abstract
For the last few years, user-generated live streaming has attracted the interests of the public, and various webcasting platforms have been established. With the popularization of mobile devices and the development of internet communication, it is easy for people to be live broadcasters. Therefore, it becomes more and more competitive for the live broadcasters to attract viewers. In this study, we aim to explore the factors that affect viewing behaviors of live streaming, and the results can provide useful references for live broadcasters when making content.
In this study, we take Twitch as the research target and use two datasets collected by different scholars in 2014 and 2015 to conduct experiments. To find out the important factors, we use iterative Random Forests (iRF) to identify the stable interactions of the factors. Because the results of iRF only show the factors and the stability score which can be treated as statistical significance, we adopt the linear regression and decision tree methods to take their advantages for unrolling the detail.
The total number of views of the channel, one of the factors in both datasets, is significant in the results obtained by linear regression. Also, decision trees show that some timezones and some games may attract more viewers. In this study, we not only explore the factors from the datasets, but also observe the local cultures that affect viewing behaviors. Our analytical results can be comprehensively explained by the theory of planned behavior, which indicates that the results have an impact on the viewing intention of viewers.
In addition, due to the various cultural backgrounds, we also explore the viewers’ preferences for categories of live streaming in different countries. The median viewer is an achievable goal, so we take the median viewer as a target when discussing categories of live streaming. By understanding the preferences of the viewers, the live broadcasters can customize the content accordingly for the most popular categories in different countries.
目次 Table of Contents
論文審定書 i
摘要 ii
Abstract iii
第一章 緒論 1
1.1 研究背景與動機 1
1.2 研究目的 1
1.2.1 影響觀看人數之因素 2
1.2.2 不同國家之喜好觀看類型 2
第二章 文獻探討 4
2.1 Twitch 4
2.2 資料探勘方法 4
2.2.1 分類、分群與關聯法則 5
2.2.2 RIT 6
2.2.3 iRF 7
2.3 串流影片分析之應用 10
第三章 研究方法 12
3.1 研究流程 12
3.2 尋找影響觀看人數之變數 14
3.2.1 迭代隨機森林 14
3.2.2 RIT 14
3.2.3 iRF穩定分數 15
3.3 iRF結果分析 16
3.3.1 線性迴歸 16
3.3.2 決策樹 16
3.4 計畫行為理論 17
第四章 研究結果與分析 20
4.1 資料集介紹與前處理 20
4.2 資料集之敘述性統計 23
4.3 iRF尋找影響變數之結果 34
4.3.1 利用2014年資料集之iRF結果 34
4.3.2 利用2015年資料集之iRF結果 38
4.4 iRF結果分析 43
4.4.1 以線性迴歸分析 43
4.4.2 以決策樹分析 46
4.4.3 綜合觀察與結果說明 65
4.5 不同國家之喜好觀看類型 74
第五章 結論與未來展望 80
5.1 結論 80
5.2 未來展望 81
參考文獻 82
附錄一 84
參考文獻 References
[1] "THE 2015 RETROSPECTIVE," https://www.twitch.tv/year/2015/.
[2] "1,600萬台灣人都在看!Twitch營運長Kevin Lin談遊戲直播的下一個未來," https://www.bnext.com.tw/article/42943/twitch-focus-on-mobile-app-and-content-in-2017.
[3] E. Freitas. "Presenting the Twitch 2016 Year in Review," 2017; https://blog.twitch.tv/presenting-the-twitch-2016-year-in-review-b2e0cdc72f18.
[4] J. Deng, F. Cuadrado, G. Tyson, and S. Uhlig, "Behind the game: Exploring the twitch streaming platform." pp. 1-6.
[5] X. Wu, V. Kumar, J. R. Quinlan, J. Ghosh, Q. Yang, H. Motoda, G. J. McLachlan, A. Ng, B. Liu, and S. Y. Philip, “Top 10 algorithms in data mining,” Knowledge and information systems, vol. 14, no. 1, pp. 1-37, 2008.
[6] A. Liaw, and M. Wiener, “Classification and regression by randomForest,” R news, vol. 2, no. 3, pp. 18-22, 2002.
[7] P. Berkhin, "A survey of clustering data mining techniques," Grouping multidimensional data, pp. 25-71: Springer, 2006.
[8] R. Agrawal, T. Imieliński, and A. Swami, "Mining association rules between sets of items in large databases." pp. 207-216.
[9] R. D. Shah, and N. Meinshausen, “Random intersection trees,” The Journal of Machine Learning Research, vol. 15, no. 1, pp. 629-654, 2014.
[10] S. Basu, K. Kumbier, J. B. Brown, and B. Yu, “iterative Random Forests to discover predictive and stable high-order interactions,” Proceedings of the National Academy of Sciences, 2017.
[11] K. Kumbier. "iterative Random Forests," https://www.stat.berkeley.edu/~kkumbier/project/irf/.
[12] K. Pires, and G. Simon, "YouTube live and Twitch: a tour of user-generated live streaming systems." pp. 225-230.
[13] M. Bärtl, “YouTube channels, uploads and views: A statistical analysis of the past 10 years,” Convergence, vol. 24, no. 1, pp. 16-32, 2018.
[14] F. Figueiredo, “On the prediction of popularity of trends and hits for user generated videos,” in Proceedings of the sixth ACM international conference on Web search and data mining, Rome, Italy, 2013, pp. 741-746.
[15] "Live Streaming Sessions Dataset," http://dash.ipv6.enstb.fr/dataset/live-sessions/.
[16] "Dataset for "Cloud-assisted Crowdsourced Livecast "," https://clivecast.github.io/.
[17] I. Ajzen, “The theory of planned behavior,” Organizational behavior and human decision processes, vol. 50, no. 2, pp. 179-211, 1991.
[18] P. A. Pavlou, and M. Fygenson, “Understanding and predicting electronic commerce adoption: An extension of the theory of planned behavior,” MIS quarterly, pp. 115-143, 2006.
[19] "Twitch直播數據調查:已成為遊戲推廣渠道," https://read01.com/NJzzMz.html.
[20] "The Most Watched Games on Twitch, June 2018," https://www.twitchmetrics.net/games/viewership.
[21] 林泳全, “視訊內容, 畫面播放率及資料傳輸率影響視訊品質量測之研究,” 2006.
[22] "Crece mercado de videojuegos en México," http://www.milenio.com/negocios/crece-mercado-de-videojuegos-en-mexico.
[23] "The Dutch online gaming industry," https://www.hollandtradeandinvest.com/key-sectors/creative-industries/online-gaming-industry.
[24] "Liquipedia StarCraft Brood War Wiki - Artosis," https://liquipedia.net/starcraft/Artosis.
[25] "韓國人的電子競技實力為何能稱霸世界?," https://kknews.cc/zh-tw/game/p8e2vqz.html.
[26] "Twitch TV全美爆紅," https://news.tvbs.com.tw/life/537018.
[27] "美國CBS新聞封面故事 競爭激烈的電競世界," https://gnn.gamer.com.tw/6/108076.html.
[28] "NCAA進軍電競?聘請市調公司調查大學電競現況," https://www.mirrormedia.mg/story/20171206game_esp_nacc/.
[29] "如何收看電競直播?台灣偏愛的Twitch 中、日、韓都不愛," https://www.mirrormedia.mg/story/20171120game_esp_stream.
[30] "Twitch Help Center - Social Eating 常見問題," https://help.twitch.tv/customer/zh_tw/portal/articles/2483343-social-eating-faq.
[31] C. Zhang, J. Liu, and H. Wang, “Cloud-assisted crowdsourced livecast,” ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), vol. 13, no. 3s, pp. 46, 2017.
電子全文 Fulltext
本電子全文僅授權使用者為學術研究之目的,進行個人非營利性質之檢索、閱讀、列印。請遵守中華民國著作權法之相關規定,切勿任意重製、散佈、改作、轉貼、播送,以免觸法。
論文使用權限 Thesis access permission:自定論文開放時間 user define
開放時間 Available:
校內 Campus: 已公開 available
校外 Off-campus: 已公開 available


紙本論文 Printed copies
紙本論文的公開資訊在102學年度以後相對較為完整。如果需要查詢101學年度以前的紙本論文公開資訊,請聯繫圖資處紙本論文服務櫃台。如有不便之處敬請見諒。
開放時間 available 已公開 available

QR Code