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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 陳銘憲(Ming-Syan Chen) | |
| dc.contributor.author | Yu-Jen Chen | en |
| dc.contributor.author | 陳俞任 | zh_TW |
| dc.date.accessioned | 2021-06-13T01:04:06Z | - |
| dc.date.available | 2011-08-08 | |
| dc.date.copyright | 2011-08-08 | |
| dc.date.issued | 2011 | |
| dc.date.submitted | 2011-08-03 | |
| dc.identifier.citation | [1] Facebook statistic:
http://www.facebook.com/press/info.php?statistics/ [2] Twitter statistic: http://blog.twitter.com/2011/03/numbers.html/ [3] Myspace statistic: http://www.bbc.co.uk/newsbeat/12862139/ [4] LinkedIn statistic: http://linkhumans.com/blog/linkedin/linkedin-global-network-statistics-2011-infographic/ [5] E. Gilbert and K. Karahalios, “Predicting Tie Strength With Social Media”, in Proc. CHI, pp.211-220, 2009. [6] Facebook: http://www.facebook.com/ [7] Twitter: http://twitter.com/ [8] LinkIn: http://www.linkedin.com/ [9] Sina Blog: http://weibo.com/ [10] H. Li, Y. Tian, W. Lee, C. L. Giles, and M. Chen, 'Personalized Feed Recommendation Service for Social Networks', in Proc. SocialCom/PASSAT, pp.96-103, 2010. [11] A. Joly, P. Maret, and J. Daigremont, 'Contextual Recommendation of Social Updates, a Tag-based Framework', in Proc. AMT, pp.436-447, 2010. [12] R. K. Pon, D. Buttler, and T. Critchlow, 'Tracking Multiple Topics for Finding Interesting Articles', in Proc. SIGKDD, pp.560-569, 2007. [13] W. Geyer, C. Dugan, D. R. Millen, M. Muller, and J. Freyne, 'Recommending Topics for Self-Descriptions in Online User Profiles', in Proc. RecSys, pp.59-66, 2008. [14] I. Guy, N. Zwerdling, D. Carmel, I. Ronen, E. Uziel, S. Yogev, and S. Ofek-Koifman, 'Personalized recommendation of social software items based on social relations', in Proc. RecSys, pp.53-60, 2009. [15] M. Kayaalp, T. Ozyer, and S. T. Ozyer, 'A Collaborative and Content Based Event Recommendation System Integrated with Data Collection Scrapers and Services at a Social Networking Site'. in Proc. ASONAM, pp.113-118, 2009. [16] A. Seth and J. Zhang, 'A Social Network Based Approach to Personalized Recommendation of Participatory Media Content', in Proc. ICWSM, pp.109-117, 2008. [17] C.-T. Li and S.-D. Lin, 'Egocentric Information Abstraction and Visualization for Heterogeneous Social Networks', in Proc. ASONAM, pp.255-260, 2009. [18] N. Du, B. Wu, X. Pei, B. Wang, and L. Xu, 'Community Detection in Large-Scale Social Networks', in Proc. WebKDD/SNAKDD, pp.16-25, 2007. [19] R. Cazabet, F. Amblard, and C. Hanachi, 'Detection of Overlapping Communities in Dynamical Social Networks', in Proc. SocialCom/PASSAT, pp.309-314, 2010. [20] Y.-R. Lin, H. Sundaram, A. Kelliher, 'Summarization of Social Activity over Time: People, Actions and Concepts in Dynamic Networks', in Proc. CIKM, pp.1979-1380, 2008. [21] Y.-R. Lin, H. Sundaram, A. Kelliher, 'Summarization of Large Scale Social Network Activity', in Proc. ICASSP, pp.3481-3484, 2009. [22] D. Gruhl, D. N. Meredith, and J. H. Pieper, 'The Web Beyond Popularity: A Really Simple System for Web Scale RSS', in Proc. WWW, pp.183-192, 2006. [23] Facebook Graph API: http://developers.facebook.com/docs/reference/api/ [24] F-measure: http://en.wikipedia.org/wiki/F-measure | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/29297 | - |
| dc.description.abstract | 隨著Facebook、Twitter等社群網站的盛行，越來越多的人習慣每天在社群網站上與朋友們互動並且閱讀最新的資訊，當使用者的朋友數量及訂閱的資訊越來越多時，人們每天會收到數以百計的資訊，並且淹沒在這些資訊海當中，使用者往往需要花費許多的時間及精神以消化這些大量的資訊，更嚴重的是，若使用者沒有閱讀完所有的訊息就可能會遺漏重要的資訊。
為了解決這個問題，我們提出了一個全新的視覺化技術，使用者可以任意的選擇欲瀏覽的時間區段，我們提供一個以使用者為中心的個人化社群網絡，並且以此社群網絡來視覺化的呈現出這個時間區段內的所有訊息，為了增進使用者的閱讀體驗，我們的系統提供了社群偵測、朋友互動分析、朋友重要性分析，使用者可以藉此了解社群網絡上的朋友結構、朋友們之間的互動情況及互動內容、以及優先的瀏覽較有興趣或重要的資訊，我們實做了一個Facebook的應用程式並利用真實的資料證明我們的系統是可行且實際的。 | zh_TW |
| dc.description.abstract | Online social network services such as Facebook and Twitter have become increasingly popular. More and more users are accustomed to regularly reading the latest news feeds and interacting with friends on these social websites. However, when the numbers of friends and subscribed pages increase to a large extent, users will receive hundreds of messages in a day and will be overwhelmed by the information overload. To alleviate this problem, we propose a novel visualization technique for social news feeds summarization on large-scale social web services. The proposed system SocFeedViewer can produce an egocentric network graph based on the news feeds generated in an arbitrary period of time. This graph provides an overview of those who have generated news feeds during this time period. To enhance the reading experience, we incorporate community detection, connectivity analysis, and importance analysis into our system to make users capable of preferentially surfing news feeds that are more significant and interesting. We implement a real-world application and use the real social data of several volunteers to verify the usefulness of SocFeedViewer. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-13T01:04:06Z (GMT). No. of bitstreams: 1 ntu-100-R98921055-1.pdf: 2633910 bytes, checksum: 4405bc0f3b688cd5a5e175c1e460cc01 (MD5) Previous issue date: 2011 | en |
| dc.description.tableofcontents | 口試委員會審定書……………………………………………………………………...#
Acknowledgements i 中文摘要 ii ABSTRACT iii CONTENTS iv LIST OF FIGURES vi LIST OF TABLES vii Chapter 1 Introduction 1 Chapter 2 Preliminaries 6 2.1 Problem Description 6 2.2 System Architecture 8 2.3 Related Work 9 Chapter 3 Visualization Interface for Social News Feeds Summarization 11 3.1 Overview of Egocentric Network Graph 11 3.2 Overview of Social News Feeds Presentation 14 Chapter 4 Visualization Interface of Egocentric Network Graph 15 4.1 Social Community Clustering Based on Friendship Information 15 4.1.1 Concept of Mutual Friends 16 4.1.2 Hierarchical Clustering Algorithm 17 4.2 Connectivity Analysis Based on Dynamic Social News Feeds 20 4.2.1 Design of Data Structure 21 4.2.2 Algorithm Details 22 4.3 Node Importance Analysis Based on Miscellaneous Information 24 4.3.1 Node Importance Assessment 25 4.3.2 Details of Miscellaneous Information 26 Chapter 5 Social News Feeds Presentation 28 Chapter 6 Performance Evaluation 31 6.1 Experimental Setup 31 6.2 Community Detection Evaluation 31 6.3 Evaluation of Node Importance Analysis 33 6.4 Issues of Storage Space and Execution Time 34 Chapter 7 Conclusion 36 REFERENCES 37 | |
| dc.language.iso | en | |
| dc.title | 整合及呈現社群網絡平台上大量社群資訊之新穎視覺化技術 | zh_TW |
| dc.title | SocFeedViewer: A Novel Visualization Technique for Social News Feeds Summarization on Large-Scale Social Network Services | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 99-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 林永松(Yeong-Sung Lin),吳尚鴻(Shan-Hung Wu),呂俊賢(Chun-Shien Lu) | |
| dc.subject.keyword | 資訊摘要,個人社群網絡,視覺化技術,社群網絡服務, | zh_TW |
| dc.subject.keyword | News Feed Summarization,Egocentric Network Graph,Visualization Technique,Social Network Service, | en |
| dc.relation.page | 39 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2011-08-04 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 電機工程學研究所 | zh_TW |
| 顯示於系所單位： | 電機工程學系 | |
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| ntu-100-1.pdf 目前未授權公開取用 | 2.57 MB | Adobe PDF |
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