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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
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dc.contributor.advisor | 鄭卜壬 | |
dc.contributor.author | Yi-Shiang Tzeng | en |
dc.contributor.author | 曾奕翔 | zh_TW |
dc.date.accessioned | 2021-06-16T13:34:42Z | - |
dc.date.available | 2015-07-30 | |
dc.date.copyright | 2013-07-30 | |
dc.date.issued | 2013 | |
dc.date.submitted | 2013-07-18 | |
dc.identifier.citation | [1] J. Allan, R. Papka, and V. Lavrenko. On-line new event detection and tracking. In
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval, 1998. [2] L. Backstrom, J. Kleinberg, R. Kumar, and J. Novak. Spatial variation in search engine queries. In Proceedings of the 17th international conference on World Wide Web, WWW ’08, pages 357–366, New York, NY, USA, 2008. ACM. [3] M. Cataldi, L. Di Caro, and C. Schifanella. Emerging topic detection on twitter based on temporal and social terms evaluation. In Proceedings of the Tenth International Workshop on Multimedia Data Mining, 2010. [4] C.-C. Chang and C.-J. Lin. LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology, 2011. [5] C. C. Chen, Y.-T. Chen, and M. C. Chen. An aging theory for event life-cycle modeling. IEEE Transactions on Systems, Man, and Cybernetics, Part A, 2007. [6] L. Chen and A. Roy. Event detection from flickr data through wavelet-based spatial analysis. In Proceedings of the 18th ACM conference on Information and knowledge management, 2009. [7] I. Daubechies. Ten lectures on wavelets. Society for Industrial and Applied Mathematics, 1 edition, June 1992. 37 [8] Q. He, K. Chang, and E.-P. Lim. Analyzing feature trajectories for event detection. In Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval, SIGIR ’07, 2007. [9] W. Jianshu and L. Bu-Sung. Event detection in twitter. In Proceedings of the Fifth International AAAI Conference on Weblogs and Social Media, 2011. [10] T. Joachims. Text categorization with support vector machines: Learning with many relevant features. Machine Learning: ECML-98, pages 137–142, 1998. [11] J. Kleinberg. Bursty and hierarchical structure in streams. In Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining, KDD ’02, pages 91–101, New York, NY, USA, 2002. ACM. [12] R. Lu, Z. Xu, Y. Zhang, and Q. Yang. Life activity modeling of news event on twitter using energy function. In Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part II, PAKDD’12, pages 73–84, Berlin, Heidelberg, 2012. Springer-Verlag. [13] S. Phuvipadawat and T. Murata. Breaking news detection and tracking in twitter. In Web Intelligence and Intelligent Agent Technology (WI-IAT), 2010 IEEE/WIC/ACM International Conference on, volume 3, pages 120–123, 2010. [14] T. Sakaki, M. Okazaki, and Y. Matsuo. Earthquake shakes twitter users: real-time event detection by social sensors. In Proceedings of the 19th international conference on World wide web, 2010. [15] Y. Yang and J. O. Pedersen. A comparative study on feature selection in text categorization. Morgan Kaufmann Publishers Inc., 1997. [16] Y. Yang, T. Pierce, and J. Carbonell. A study of retrospective and on-line event detection. In Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval, SIGIR ’98, pages 28–36, New York, NY, USA, 1998. ACM. [17] E. Yom-Tov and F. Diaz. Out of sight, not out of mind: on the effect of social and physical detachment on information need. In Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval, 2011. [18] H. Zhang, M. Korayem, D. J. Crandall, and G. LeBuhn. Mining photo-sharing websites to study ecological phenomena. In Proceedings of the 21st international conference on World Wide Web, WWW ’12, pages 749–758, New York, NY, USA, 2012. ACM. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/62221 | - |
dc.description.abstract | 隨著Web 2.0 的興起,越來越多的使用者透過微網誌發佈訊息,推
特(Twitter) 正是其中一個代表。從2006 年創立至2012 年,它已擁有 超過五億個活躍的使用者遍及世界各地。假使我們將每個使用者當 成一個感應器(Sensor),那我們幾乎可以即時的知道這個世界正發生什 麼。這個研究的主旨是利用推特資料偵測事件以及它持續發生的時間, 並以下雨這個事件當作評估的對象。我們的目標是建立一個虛擬氣象系 統即時告訴使用者一個地方是否正在下雨。 在實作中我們的系統包含了兩個階段。在第一個階段我們建立了一 個分類器(Classifier) 去找出和下雨相關的Tweet。第二個階段我們利用 老化理論(Aging theory)的概念建立了一個用來偵測以及模擬下雨生 命週期的模型。在我們的實驗中,我們提出了兩個事件偵測的方法當 成baseline 和系統做比較。實驗結果說明事件期間偵測的問題並不能 被許多事件偵測的方法所取代。我們也對我們的系統做了一些參數的 調整,並觀察在不同情境下效能的變化。最後我們將虛擬氣象觀測系 統更進一步延伸了預測的功能。 | zh_TW |
dc.description.abstract | Twitter, a popular microblogging service, has passed the 500 million users.
If we consider widely distributed Twitter users as social sensors, the sensor network provides us a snapshot of the real world. In this study, we take the advantage of Twitter data to detect events and model their durations. We choose rain as our target event, and build an on-line weather station to tell whether it rains or not for any given location and time. Our system contains two stages. In the first stage, we find out truly rain-related tweets from candidate pool to deal with the inherent noises in Twitter. In the second stage we construct an aging based model to simulate the life cycles of rain events. We compare our model to other event detection based methods. Our results show that it’s not feasible to transform the problem of detecting duration of rain events to multiple rain events detection problems. We further figure out how spatiotemporal factors and the properties of events influence our model. User behaviour is also carefully discussed. Finally, we extend the rain event detection system to rain forecast system. | en |
dc.description.provenance | Made available in DSpace on 2021-06-16T13:34:42Z (GMT). No. of bitstreams: 1 ntu-102-R00944020-1.pdf: 1196470 bytes, checksum: d1c395af379811209e060b9483d49d44 (MD5) Previous issue date: 2013 | en |
dc.description.tableofcontents | 口試委員會審定書iii
誌謝v 摘要vii Abstract ix 1 Introduction 1 2 Related Work 5 3 Problem Specification 7 4 The Virtual Weather Station 9 5 Tweet Filtering Stage 11 6 Event Detection and Modelling Stage 13 6.1 Nutrition Calculation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 6.1.1 Uniform Nutrition . . . . . . . . . . . . . . . . . . . . . . . . . 14 6.1.2 Weighted Nutrition . . . . . . . . . . . . . . . . . . . . . . . . . 14 6.2 Energy Transformation . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 6.3 Stages Identification . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 7 Experiments 23 7.1 Evaluation of Filtering . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 7.2 Evaluation of Event Detection . . . . . . . . . . . . . . . . . . . . . . . 24 7.2.1 On-line Rain Detection System . . . . . . . . . . . . . . . . . . 25 7.2.2 Parameter Tuning . . . . . . . . . . . . . . . . . . . . . . . . . . 28 7.2.3 Case Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 7.3 User Behavior Analysis on Rain Events . . . . . . . . . . . . . . . . . . 30 8 Rain Prediction 33 9 Conclusion 35 Bibliography 37 | |
dc.language.iso | en | |
dc.title | 利用微網誌資訊偵測事件期間 | zh_TW |
dc.title | Event Duration Detection on Microblogging | en |
dc.type | Thesis | |
dc.date.schoolyear | 101-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 陳信希,張嘉惠,盧文祥 | |
dc.subject.keyword | 事件偵測,事件期間,微網誌, | zh_TW |
dc.subject.keyword | Event Detection,Event Duration,Microblogging, | en |
dc.relation.page | 39 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2013-07-18 | |
dc.contributor.author-college | 電機資訊學院 | zh_TW |
dc.contributor.author-dept | 資訊網路與多媒體研究所 | zh_TW |
顯示於系所單位: | 資訊網路與多媒體研究所 |
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