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。

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.

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

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