近年來，出現了一種新型媒體“直播”，透過串流的技術，直播可以讓人們向他人展示自己的生活，並在低延遲的環境下與他人即時的互動，這種新型態的媒體極大的改善了傳播者和受眾之間的用戶體驗。台灣有著許多直播平台，其中以Twitch最為著名，在2018年，全球每天平均有1500萬人觀看Twtich上的直播，而這些人會帶來龐大的商機。
因此，在本研究中，我們欲探索Twitch平台上的實況主和觀眾之間的關係，目的是預測觀眾是否願意成為訂閱者而付費給平台與實況主。本研究使用Twitch API和網絡爬蟲來收集數據，利用文本挖掘來捕捉觀眾在直播聊天室中的行為，並標記普通觀眾和訂閱者。接著我們使用一些監督式學習的方法，如邏輯式回歸，SVM，決策樹和隨機森林，來構建模型進行預測。
最終模型的準確度可達到0.7366，而資料蒐集的區間越短，準確率會越高。
觀看者在聊天室裡留言的頻率、是否開啟直播的通知、觀看者的追隨直播主的數量以及觀看者在聊天室留言的負面情緒對於訂閱直播主有著顯著影響。

In recent years, there has been a new type of media, “live stream”. Through streaming technology, it can make people show their lives to others and interact with others at low latency. This kind of media greatly improve the user experience between communicators and audience. Among many live stream platforms, Twitch is most famous in Taiwan. In 2018, there were an average of 15 million people watching live stream on Twtich every day . A large number of people will bring a lot of business opportunities.
Therefore, in this study, we want to explore the relationship between the live streamers and viewers on the Twitch platform. We want to predict whether viewers will be willing to pay for subscriptions. This study uses Twitch API and web crawler to collect data. We use text mining to catch viewers’ actions in live stream’s chat room and label general viewers and subscribers. And then, we use some supervised learning methods, such as logistic regression, SVM, decision tree and random forest, to build models to predict.
The best model’s accuracy could reach 0.73664. And the result indicates that the chat frequency, live stream’s notification and the number of viewer’s following influence whether the viewer will subscribe the streamer.

論文審定書 i
論文公開授權書 ii
中文摘要 iii
Abstract iv
Chapter 1. Introduction 1
1.1 Research Background 1
1.2 Research Motivation 3
1.3 Research Purpose 6
Chapter 2. Literature Review 7
2.1 Live streaming 7
2.2 Live chatting 7
2.3 The categories of viewers on Twitch 8
2.4 Donation/Subscription intention 9
2.5 Text mining 10
2.6 Classification method 12
Chapter 3. Method 17
3.1 Data Collecting 17
3.2 Data Preprocessing 19
3.3 Data Splitting 21
3.4 Data Mining 21
Chapter 4. Result 23
4.1 Descriptive statistics 23
4.2 Model performance 24
4.2.1 Original models 24
4.2.2 Stepwise models 25
4.2.3 Committee machine 26
4.2.4 Logistic coefficient analysis 27
4.3 Discussion 27
Chapter 5. Conclusion 29
5.1 Implications for research 29
5.2 Implications for practice 29
5.3 Limitations and future research 30
References 31
Appendix 37

4Gamers編輯部. (2015). 台北Twitch觀看時間世界第一. Retrieved from https://www.4gamers.com.tw/news/detail/5190/21714
Agresti, A. (2002). Categorical Data Analysis Second Edition, Wiley & Sons, Inc., Hoboken, New Jersey, 165-210.
Antonio, H. (2018). Tipping up 33%, Twitch viewers up 21%, Fortnite dominates — Q1'18 Streamlabs Report. Retrieved from https://blog.streamlabs.com/tipping-up-33-twitch-viewers-up-21-fortnite-dominates-q118-streamlabs-report-52f60450af5a
Bree, B. (2015). Twitch Claims 43% Of Revenue From $3.8 Billion Gaming Content Industry. Retrieved from https://www.tubefilter.com/2015/07/10/twitch-global-gaming-content-revenue-3-billion/
Carla, M. (2015). By 2019, 80% of the World’s Internet Traffic Will Be Video [Cisco Study]. Retrieved from https://tubularinsights.com/2019-internet-video-traffic/#ixzz4JU3cxVXv
Chen, C. -C. & Lin Y. -C. (2018). What drives live-stream usage intention? The perspectives of flow, entertainment, social interaction, and endorsement. Telematics and Informatics, 35, 293-303.
Cortes, C. & Vapnik, V. (1995). Support-Vector Networks. Machine Learning, 20 (3), 273-297.
Daugherty, T., Eastin M. S. & Bright L. (2008). Exploring consumer motivations for creating user generated content. Journal of Interactive Advertising, 8(2), 16-25.
David, K. (2008). TEEN KILLS SELF ON JUSTIN.TV – UPDATE. Retrieved from https://www.wired.com/2008/11/teen-kills-self/
Dennis, R., Jenny, S. & Jeremy, D. (2014). Twitch plays Pokemon: A case study in big G games. Proceedings of DiGRA, 3(12).
Draper, N., Smith, H. (1980). Applied regression analysis, second edition. New York, NY, USA: John Wiley & Sons.
Freitas, E. (2018). 2017 Twitch Year in Review: The Comic Book. Retrieved from https://blog.twitch.tv/2017-twitch-year-in-review-the-comic-book-ed1b08f8f116
Haykin, S. (1998). Neural Networks: A Comprehensive Foundation. Prentice Hall PTR, Upper Saddle River, NJ.
Hilvert-Bruce, Z., Neill, J. T., Sjöblom, M., & Hamari, J. (2018). Social motivations of live-streaming viewer engagement on Twitch. Computers in Human Behavior, 84, 58–67.
Hu, M., Zhang, M. & Wang, Y. (2017). Why do audiences choose to keep watching on live video streaming platforms? An explanation of dual identification framework. Computers in Human Behavior, 75, 594-606.
Jensen, J. & Toscan, C. (1999). Interactive Television: TV of the Future or the Future of TV ?. Aalborg: Aalborg University Press
Kamiński, B., Jakubczyk, M. & Szufel, P. (2017). A framework for sensitivity analysis of decision trees. Central European Journal of Operations Research, 26(1), 135-159.
Katz, E., Blumler, J. G., & Gurevitch, M. (1973). Uses and Gratifications Research. The Public Opinion Quarterly, 37(4), 509-523.
Kemp, S. (2018). Digital in 2018: world’s internet users pass the 4 billion mark. Retrieved from https://wearesocial.com/uk/blog/2018/01/global-digital-report-2018
Kim, J.-Y., Natter, M. & Spann, M. (2009). Pay what you want: A new participative pricing mechanism. Journal of Marketing, 73(1), 44-58.
Kole, R. (2012). 18 Marketing Statistics And What It Means For Video Marketing. Retrieved from https://www.videobrewery.com/blog/18-video-marketing-statistics/
Ku, L.-W. & Chen, H.-H. (2007). Mining opinions from the web: Beyond relevance retrieval. Journal of American Society for Information Science and Technology, Special Issue on Mining Web Resources for Enhancing Information Retrieval, 58(12), 1838-1850.
Kunter, M. (2015). Exploring the pay-what-you-want payment motivation. Journal of Business Research, 68 (11), 2347–2357.
Lee, M.R., Yen, D.C. & Hsiao, C.Y. (2014). Understanding the perceived community value of Facebook users. Computers in Human Behavior, 35, 350–358.
Liang, T.-P. & Turban, E. (2011). Introduction to the special issue social commerce: a research framework for social commerce. International Journal of Electronic Commerce, 16(2), 5-13.
Liaw, A & Wiener, M. (2002). Classification and Regression by Random Forest. R News, 2(3), 18-22.
Meulen, A.t. (2017). Logic and Natural Language. in Goble, L., ed. The Blackwell Guide to Philosophical Logic. Blackwell.
Most participants on a single-player online videogame. (2014). Retrieved from https://www.guinnessworldrecords.com/world-records/most-participants-on-a-single-player-online-videogame/
Pires, K., & Simon, G. (2014). DASH in Twitch. Proceedings of the 2014 Workshop on Design. Quality and Deployment of Adaptive Video Streaming - VideoNext ’14.
Raes, T. C. M. (2015). Twitch TV: motives and interaction, a consumer perspective. Aalborg University Faculty of Humanities Master in CCG 10th semester, Master thesis.
Sjöblom, M., & Hamari, J. (2017). Why do people watch others play video games? An empirical study on the motivations of Twitch users. Computers in Human Behavior, 75, 985–996.
Sjöblom, M., Törhönen, M., Hamari, J. & Macey, J. (2017). Content structure is king: An empirical study on gratifications, game genres and content type on Twitch. Computers in Human Behavior, 73, 161-171.
Sjöblom, M., Törhönen, M., Hamari, J. & Macey, J. (2019). The ingredients of Twitch streaming: Affordances of game streams. Computers in Human Behavior, 92, 20-28.
Smith, T., Obrist, M., & Wright, P. (2013). Live-streaming changes the (video) game. Proceedings of the 11th European Conference on Interactive TV and Video - EuroITV ’13.
Thomson, M., MacInnis, D. J., & Whan Park, C. (2005). The ties that bind: Measuring the strength of consumers’ emotional attachments to brands. Journal of Consumer Psychology, 15(1), 77–91.
Twitch Global Analytic Stats 2017. (2017). Retrieved from https://twitchstats.net/global-twitch-analytics/2017
Video Streaming Market Size, Share & Trends Analysis Report By Streaming Scope, By Solution Scope, By Platform Scope, By Revenue Model, By Service, By Deployment, By User, And Segment Forecasts, 2019 – 2025. (2019). Retrieved from https://www.grandviewresearch.com/industry-analysis/video-streaming-market
Wan, J., Lu, Y., Wang, B., & Zhao, L. (2017). How attachment influences users’ willingness to donate to content creators in social media: A socio-technical systems perspective. Information & Management, 54(7), 837–850.
Wu, X., Kumar, V., Ross Quinlan, J., Ghosh, J., Yang, Q., Motoda, H., … Steinberg, D. (2007). Top 10 algorithms in data mining. Knowledge and Information Systems, 14(1), 1–37.
Zhang, H. (2004). The Optimality of Naive Bayes . Proceedings of the Seventeenth International Florida Artificial Intelligence Research Society Conference.
Zhao, F.-Y., Kong ,Y. (2017). Discovering social network key opinion leaders based on a psychological influence model. International Journal of Management and Applied Science, 3(9), ISSN: 2394-7926.
林奕辰（2017）。影響閱聽人觀看網路直播意圖之因素研究。國立中興大學資訊管理學系所碩士論文，台中市。 取自https://hdl.handle.net/11296/7st92q