臺灣博碩士論文加值系統

近年來社交網路服務蓬勃發展，吸引大量的使用者投入許多時間在其中，為了解在使用者互動過程中是否有學習情形產生，本研究檢視廣受歡迎的社交網路服務–推特，以推特社群內一位活耀的使用者–Tim O’Reilly先生為研究對象並在他同意下，以他於二零一一年十月一日至十月三十一日期間所發表的四百六十三則推文，以及六百一十七位推特使用者回覆的推文作為本研究分析的主要內容。

本研究方法採取質化為主、量化為輔，質性研究以內容分析法分析 O’Reilly 先生的推文以及其他推特使用者的回覆，量化方面以敘述性統計分析O’Reilly 先生每則推文的回覆與轉推次數。研究結果發現在每則推文一百四十字元的限制下，以及推特語法 (Twitter syntax) 與關注 (follow) 網路的支援下，推特使用者們得以透過對話來學習。而在對話中，可歸納出三種學習行為，包含：1) 分享連結與資源、2)提供答案與解決辦法以及3) 發表評論與個人意見。總言之，本研究顯示在推特上的學習具有：1) 動態，2) 不可預測與3) 偶發的特性，且通常主要被三種議題觸發：1) 專業人士分享的專業議題，2) 受歡迎的意見領袖發表的小眾與冷門議題，以及 3) 熱門且重要的國際性議題。

本研究首先嘗試從日常推文中進行實證研究，探討在使用者互動過程中自然產生的學習情形，並由研究結果得知推特上的學習不同於傳統所認知的學習定義，而歸納出上述資訊時代新穎的學習特色。建議未來仍可朝不同學習面向著手，例如針對提及熱門議題的主題標籤 (hashtag) 之推文，探討是否有不同的學習情形產生。

Recent years, numerous users spend a bunch of time engaging in social networking services (SNSs). To explore whether learning occurred among users’ interactions, this study investigated an active tweeter (Twitter user), Mr. Tim O’Reilly, on a popular SNS, Twitter, as the research participant. Under his permission, the researcher collected Tim’s 463 tweets as well as tweets replied from other 617 tweeters for analysis from 1st October to 31th October 2011.

This study mainly used qualitative methodology and supplemented by quantitative one. More specifically, the researcher conducted qualitative content analysis on Tim’s tweets and others’ reply tweets, and used descriptive statistics to calculate times of replies and retweets to Tim’s tweets. The results show that tweeters learn through conversations with the aid of 140-character limit per tweet, Twitter syntaxes and following networks. Three learning behaviors were generalized from conversations, including 1) sharing links and resources, 2) providing answers and solutions, and 3) making comments and adding personal opinions. To conclude, this study demonstrated that on Twitter learning is 1) dynamic, 2) unpredictable and 3) incidental generally triggered by 1) professional issues shared by experts, 2) minority and less popular issues shared by popular opinion leaders, and 3) hot and important issues around the world.

This study first tried to provide empirical evidence on daily tweets and investigated the learning taken place naturally among tweeters’ interactions. The results show that the learning on Twitter is different from the traditional learning that we used to know, and conclude the novel learning features mentioned above in the Information age. It is recommended for the future researchers to probe into tweets mentioned hashtags that relate to popular and well-known theme-based topics to see whether a different form of learning will be triggered.

Chinese Abstract i
English Abstract ii
Acknowledgements iv
Table of Contents vi
List of Figures viii
List of Tables ix
CHAPTER 1 Introduction 1
1.1 Research Background and Motivation 1
1.2 Purpose of the Study and Research Questions 7
1.3 Significance of the Study 8
1.4 Outline of the Thesis 9
CHAPTER 2 Literature Review 10
2.1 Shifting Social Networking Service (SNS) toward Facilitating Learning 10
2.1.1 Inner World beyond SNS 11
2.1.2 The Learning Affordances of SNS 12
2.2 What Users Could Benefit from Twitter 14
2.2.1 Facet of Socializing 14
2.2.1.1 Insight into Tweeters 15
2.2.1.2 Asymmetric Follow Model 19
2.2.2 Facet of Microblogging 20
2.2.2.1 The 140-character Limit 21
2.2.2.2 Twitter Syntax 23
2.3 Learning on Twitter 29
2.3.1 Social Learning 31
2.3.2 Incidental Learning 34
2.3.3 Adult Learner 36
2.3.4 The Learning Affordances of Twitter 38
CHAPTER 3 Methodology 41
3.1 Research Design 41
3.2 Participants 42
3.3 Data Collection 43
3.4 Coding 46
3.5 Text Processing 48
CHAPTER 4 Results and Discussion 50
4.1 Tweet Analysis 50
4.2 What Types of Tweets will Engage Tweeters? 52
4.2.1 Retweet 52
4.2.2 Reply 55
4.3 Do Tweeters Learn from Others through Daily Interactions? 58
4.3.1 Conversations 58
4.3.2 Learning through Conversations 60
4.3.3 Learning through Following 64
4.4 What Are the Learning Affordances of Twitter? 69
4.4.1 Twitter Syntax 70
4.4.2 Learning by Following Experts 71
4.4.3 The Long-tail Effect in Learning 75
4.4.4 Global-view-based Learning 77
CHAPTER 5 Conclusions and Future Research 81
5.1 Conclusions 81
5.1.1 Engagement through Retweets and Replies 81
5.1.2 They Tweet, They Learn 82
5.1.3 Learning Affordances of Twitter 84
5.2 Limitations 85
5.3 Future Research 85
REFERENCES 86
APPENDIX 94

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