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研究生: 林英傑
Ying-Jie Lin
論文名稱: 使用啟發式原則找出具影響力的微網誌作者與訊息傳播路徑
Using Heuristic Methods to Find Influential Microbloggers and Information Propagation Paths
指導教授: 蔡孟峰
Meng-Feng Tsai
口試委員:
學位類別: 碩士
Master
系所名稱: 資訊電機學院 - 資訊工程學系
Department of Computer Science & Information Engineering
畢業學年度: 98
語文別: 英文
論文頁數: 64
中文關鍵詞: 微網誌社會網絡社會影響力病毒式行銷
外文關鍵詞: Microblog, Viral Marketing, Social Influence, Social Network
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  • 全球網際網路的出現,讓人跟人之間有了新的聯繫方式。研究者得以在虛擬空間中觀察與模擬出部份社會網絡的特性。針對Social Network Services 的研究很多,部份關注在網絡中口碑的傳遞,這類研究對於強化病毒式行銷的成效有很大的幫助。本研究也關注於一段訊息、概念是如何在社會網絡中散播。我們選擇微網誌作為觀察的對象,跟其他的Social Network Services 不同,微網誌具有訊息公開,互動頻繁且多樣等特性。加上有許多公司採用微網誌來進行行銷,觀察微網誌上的訊息擴散將可以更深入了解訊息的傳播是否存在一些更有效率的方式。
    本研究將試圖找出具有影響力的人物並量化該人物影響力,我們定義一個使用者的影響力為使用者的一段訊息引起其他人討論的機率或是速率。也就是耗費的時間越短同時引起的討論越多的人就是有影響力的人。藉由將使用者的影響力量化,讓不同使用者的影響力可以在一定範圍內進行比較,同時利用Frequent Itemset 發覺同一類型的訊息常見的傳播路徑。藉由上述的成果,本研究可以幫助行銷人員找出訊息在網路中要透過誰來傳遞才是最有效率的,藉以找出適合作為病毒式行銷中初期體驗者的人選。


    Because of the emergence of world wide web, people have a new interactive way. The researchers can also easily observe parts of the features of social networks in the Internet.
    Some of the studies related to social network services are about the word of mouth marketing. This study is also concerned about the spreading of the messages and the concepts on the Internet. We choose microblogs as the objects to observe. Different from other social network services, microblog has the features that information is public and interaction is frequently. The goal of the research is to determine whether there is a more efficient way for spreading the message by observing the information diffusion on the microblogs.
    This study attempts to identify the influential people and to quantify the influence of the people in different themes. We define a user''s influence as the speed or the probability of one of his message to cause another user to discuss. In other words, The study quantify the influence of the users by calculating transfer rate or success ratio of the message and make the influence of different users be able to be compared.
    Finally, the study can help users find some of the microbloggers who delivers messages through the networks more efficiently, and we can use frequent itemset to find the possible paths of the transmission of a message.

    Chinese Abstract...i English Abstract...ii Acknowledgements...iv Table of Contents...v List of Figures...vii List of Tables...ix Chapter 1. INTRODUCTION...1 1.1. Motivation...1 1.2. Influence and Information Propagation...2 1.3. Contribution...3 Chapter 2. BACKGROUND AND RELATED WORK ...6 2.1. Information Propagation...6 2.2. Social Influence and Community...8 2.3. Social Influence in Twitter...9 2.4. Quantifying Influence...10 Chapter 3. MICROBLOG...11 3.1. Twitter...12 3.2. Twitter API...13 Chapter 4. PROBLEM DEFINITION...18 4.1. Social Network...18 4.2. Influence and Information Propagation...21 4.3. Information Propagation Graph...22 Chapter 5. METHODOLOGY...27 5.1. Influential Microbloggers...27 5.2. Direct Influence...28 5.2.1. Static Probability Models...28 5.2.2. Continuous Decay Models...29 5.2.3. Discrete Decay Models...31 5.2.4. Speed Models...32 5.3. Frequent Propagation Paths...33 5.3.1. Frequent Itemsets...33 5.3.2. Apriori...33 5.4. System Architecture...35 Chapter 6. EXPERIEMTAL RESULTS...36 6.1. Influential Microbloggers...36 6.2. Frequent Propagation Paths...46 Chapter 7. CONCLUSION...49 Chapter 8. FUTURE WORKS...50 Chapter 9. REFERENCES...51

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