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題名 基於堆疊圖方式之社群媒體階層式議題的視覺化探索架構
TopicWave: Visually Exploring Topics of Hierarchical Time-Oriented Data作者 熊凱文
Hsiung, Kai Wen貢獻者 紀明德
Chi, Ming Te
熊凱文
Hsiung, Kai Wen關鍵詞 資訊視覺化
資訊擴散
社群媒體
Data Visualization
Information Diffusion
Social Media日期 2015 上傳時間 1-Mar-2016 10:40:35 (UTC+8) 摘要 如何透過視覺化探索勢力消長情形，是近年來頻繁被探討的問題，常見之做法會針對帶有時間屬性的時間關聯資料 (time-oriented data)來進行觀察，而以社群媒體為例，重大議題通常是透過意見領袖提出具有關鍵性之觀點，而得以分歧出新議題並吸引其他社群媒體上之閱聽人加入討論，上述之過程牽涉評論之階層資料其層次隨著時間變化分歧與合併，然而，能夠透過視覺化之方式同時觀察上述特性有其挑戰性。本篇論文將針對階層式資料提出一套整合方式，稱為TopicWave，特別是帶有時間變化屬性的資料，希望透過改良動態圖形視覺化工具，結合 Sunburst 與 ThemeRiver Graph，實作 Facebook 上公開文章之評論(comments)行為隨時間變化的趨勢，而透過直覺式互動功能之設計。透過案例分析和使用者測試，本論文提出的方法能清楚呈現評論關係隨時間之變化與階層式結構，達到組合式創新之效果。
In recent research, it is a frequently asked question about how to explore the topic trend during a time interval. If we want to analysis and discuss this question, time-oriented data will be the most appropriate dataset. For example, on social media platform, major issues are commonly formed by opinion leaders, people will be attracted by opinion leaders and join in the commentary on a topic. The above-mentioned procedure will involve in commentary hierarchy level increasing or decreasing while time changes, however, it is challenging when we want to explore these properties using traditional visualization techniques. We propose TopicWave, a visualization design that combines ThemeRiver Graph (time-oriented visualization) and Sunburst (hierarchical data visualization). It can visualize the trend of a post’s comment on Facebook Page. TopicWave can clearly present hierarchy and time-varying trend of a Facebook post’s comment data at the same time through the intuitive design of interactive on visualization.參考文獻 [1] Aigner, W., Miksch, S., Schumann, H., & Tominski, C. (2011). Visualization of time-oriented data. Springer Science & Business Media.[2] Jürgensmann, S., & Schulz, H. J. (2010). Poster: A visual survey of tree visualization. In Proceedings of IEEE Information Visualization (Vol. 5, p. 7)[3] Byron, L., & Wattenberg, M. (2008). Stacked graphs–geometry & aesthetics.Visualization and Computer Graphics, IEEE Transactions on, 14(6), 1245-1252.[4] Rzeszotarski, J. M., & Kittur, A. (2014, April). Kinetica: Naturalistic multi-touch data visualization. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (pp. 897-906). ACM.[5] Kondo, B., & Collins, C. M. (2014). Dimpvis: Exploring time-varying information visualizations by direct manipulation. Visualization and Computer Graphics, IEEE Transactions on, 20(12), 2003-2012.[6] Rind, A., Lammarsch, T., Aigner, W., Alsallakh, B. and Miksch, S. (2013). TimeBench: A Data Model and Software Library for Visual Analytics of Time-Oriented Data. IEEE Trans. Visual. Comput. Graphics, 19(12), pp.2247-2256.[7] Dou, W., Yu, L., Wang, X., Ma, Z., & Ribarsky, W. (2013). Hierarchicaltopics: Visually exploring large text collections using topic hierarchies. Visualization and Computer Graphics, IEEE Transactions on, 19(12), 2002-2011.[8] Liu, S., Wu, Y., Wei, E., Liu, M., & Liu, Y. (2013). Storyflow: Tracking the evolution of stories. Visualization and Computer Graphics, IEEE Transactions on, 19(12), 2436-2445.[9] Xu, P., Wu, Y., Wei, E., Peng, T. Q., Liu, S., Zhu, J. J., & Qu, H. (2013). Visual analysis of topic competition on social media. Visualization and Computer Graphics, IEEE Transactions on, 19(12), 2012-2021.[10] Stasko, J., & Zhang, E. (2000). Focus+ context display and navigation techniques for enhancing radial, space-filling hierarchy visualizations. InInformation Visualization, 2000. InfoVis 2000. IEEE Symposium on (pp. 57-65). IEEE.[11] Tufte, E. R. (2006). Beautiful evidence. New York.[12] Stasko, J., Catrambone, R., Guzdial, M., & McDonald, K. (2000). An evaluation of space-filling information visualizations for depicting hierarchical structures.International Journal of Human-Computer Studies, 53(5), 663-694.[13] Wang, X., Liu, S., Song, Y., & Guo, B. (2013, August). Mining evolutionary multi-branch trees from text streams. In Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 722-730). ACM.[14] Zhang, D., Zhai, C., Han, J., Srivastava, A., & Oza, N. (2009). Topic modeling for OLAP on multidimensional text databases: topic cube and its applications.Statistical Analysis and Data Mining: The ASA Data Science Journal, 2(5‐6), 378-395.[15] Blundell, C., Teh, Y. W., & Heller, K. A. (2012). Bayesian rose trees. arXiv preprint arXiv:1203.3468.[16] Liu, X., Song, Y., Liu, S., & Wang, H. (2012, August). Automatic taxonomy construction from keywords. In Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 1433-1441). ACM.[17] Wang, X., Liu, S., Song, Y., & Guo, B. (2013, August). Mining evolutionary multi-branch trees from text streams. In Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 722-730). ACM.[18] Cui, W., Liu, S., Wu, Z., & Wei, H. (2014). How hierarchical topics evolve in large text corpora. Visualization and Computer Graphics, IEEE Transactions on, 20(12), 2281-2290.[19] Furnas, G. W. (1986). Generalized fisheye views (Vol. 17, No. 4, pp. 16-23). ACM.[20] Li, Y., Sun, J., & Shum, H. Y. (2005). Video object cut and paste. ACM Transactions on Graphics (TOG), 24(3), 595-600.[21] Kriglstein, S., Pohl, M., & Stachl, C. (2012, July). Animation for time-oriented data: An overview of empirical research. In Information Visualisation (IV), 2012 16th International Conference on (pp. 30-35). IEEE[22] Johnson, B., & Shneiderman, B. (1991, October). Tree-maps: A space-filling approach to the visualization of hierarchical information structures. InVisualization, 1991. Visualization`91, Proceedings., IEEE Conference on (pp. 284-291). IEEE.[23] Wattenberg, M. (1999, May). Visualizing the stock market. In CHI`99 extended abstracts on Human factors in computing systems (pp. 188-189). ACM.[24] Van Wijk, J. J., & Van de Wetering, H. (1999). Cushion treemaps: Visualization of hierarchical information. In Information Visualization, 1999.(Info Vis` 99) Proceedings. 1999 IEEE Symposium on (pp. 73-78). IEEE.[25] Rubinstein, M., Gutierrez, D., Sorkine, O., & Shamir, A. (2010, December). A comparative study of image retargeting. In ACM transactions on graphics (TOG)(Vol. 29, No. 6, p. 160). ACM. 描述 碩士
國立政治大學
資訊科學學系
102753015資料來源 http://thesis.lib.nccu.edu.tw/record/#G0102753015 資料類型 thesis dc.contributor.advisor 紀明德 zh_TW dc.contributor.advisor Chi, Ming Te en_US dc.contributor.author (Authors) 熊凱文 zh_TW dc.contributor.author (Authors) Hsiung, Kai Wen en_US dc.creator (作者) 熊凱文 zh_TW dc.creator (作者) Hsiung, Kai Wen en_US dc.date (日期) 2015 en_US dc.date.accessioned 1-Mar-2016 10:40:35 (UTC+8) - dc.date.available 1-Mar-2016 10:40:35 (UTC+8) - dc.date.issued (上傳時間) 1-Mar-2016 10:40:35 (UTC+8) - dc.identifier (Other Identifiers) G0102753015 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/81526 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 資訊科學學系 zh_TW dc.description (描述) 102753015 zh_TW dc.description.abstract (摘要) 如何透過視覺化探索勢力消長情形，是近年來頻繁被探討的問題，常見之做法會針對帶有時間屬性的時間關聯資料 (time-oriented data)來進行觀察，而以社群媒體為例，重大議題通常是透過意見領袖提出具有關鍵性之觀點，而得以分歧出新議題並吸引其他社群媒體上之閱聽人加入討論，上述之過程牽涉評論之階層資料其層次隨著時間變化分歧與合併，然而，能夠透過視覺化之方式同時觀察上述特性有其挑戰性。本篇論文將針對階層式資料提出一套整合方式，稱為TopicWave，特別是帶有時間變化屬性的資料，希望透過改良動態圖形視覺化工具，結合 Sunburst 與 ThemeRiver Graph，實作 Facebook 上公開文章之評論(comments)行為隨時間變化的趨勢，而透過直覺式互動功能之設計。透過案例分析和使用者測試，本論文提出的方法能清楚呈現評論關係隨時間之變化與階層式結構，達到組合式創新之效果。 zh_TW dc.description.abstract (摘要) In recent research, it is a frequently asked question about how to explore the topic trend during a time interval. If we want to analysis and discuss this question, time-oriented data will be the most appropriate dataset. For example, on social media platform, major issues are commonly formed by opinion leaders, people will be attracted by opinion leaders and join in the commentary on a topic. The above-mentioned procedure will involve in commentary hierarchy level increasing or decreasing while time changes, however, it is challenging when we want to explore these properties using traditional visualization techniques. We propose TopicWave, a visualization design that combines ThemeRiver Graph (time-oriented visualization) and Sunburst (hierarchical data visualization). It can visualize the trend of a post’s comment on Facebook Page. TopicWave can clearly present hierarchy and time-varying trend of a Facebook post’s comment data at the same time through the intuitive design of interactive on visualization. en_US dc.description.tableofcontents 摘要 iAbstract ii目錄 iii圖目錄 v第一章 緒論 11.1 研究動機與目的 11.2 問題描述 21.3 論文貢獻 31.4 論文章節架構 3第二章 相關研究 42.1 時變資料圖形生成與應用 42.2 階層資料之隱性表達方式改良 72.3 時間序列與階層式資料結構化標準 8第三章 研究方法與步驟 113.1 系統架構 113.2 時變資料視覺化 123.3 階層資料視覺化 203.4 時間維度變化呈現與直覺式互動設計 25第四章 實驗結果與討論 304.1 實作與實驗環境 314.2 實作與實驗結果 314.2.1 布魯塞爾恐怖攻擊警戒於CNN粉絲專頁貼文 314.2.2 Facebook更換大頭貼響應恐怖攻擊事件於CNN粉絲專頁貼文 334.2.3 布魯塞爾恐怖攻擊警戒於BBC News粉絲專頁貼文 34第五章 評估方法 365.1 先導性研究測試 365.2 正式研究測試 39第六章 結論與未來發展 44參考文獻 47 zh_TW dc.format.extent 2892340 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0102753015 en_US dc.subject (關鍵詞) 資訊視覺化 zh_TW dc.subject (關鍵詞) 資訊擴散 zh_TW dc.subject (關鍵詞) 社群媒體 zh_TW dc.subject (關鍵詞) Data Visualization en_US dc.subject (關鍵詞) Information Diffusion en_US dc.subject (關鍵詞) Social Media en_US dc.title (題名) 基於堆疊圖方式之社群媒體階層式議題的視覺化探索架構 zh_TW dc.title (題名) TopicWave: Visually Exploring Topics of Hierarchical Time-Oriented Data en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) [1] Aigner, W., Miksch, S., Schumann, H., & Tominski, C. (2011). Visualization of time-oriented data. Springer Science & Business Media.[2] Jürgensmann, S., & Schulz, H. J. (2010). Poster: A visual survey of tree visualization. In Proceedings of IEEE Information Visualization (Vol. 5, p. 7)[3] Byron, L., & Wattenberg, M. (2008). Stacked graphs–geometry & aesthetics.Visualization and Computer Graphics, IEEE Transactions on, 14(6), 1245-1252.[4] Rzeszotarski, J. M., & Kittur, A. (2014, April). Kinetica: Naturalistic multi-touch data visualization. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (pp. 897-906). ACM.[5] Kondo, B., & Collins, C. M. (2014). Dimpvis: Exploring time-varying information visualizations by direct manipulation. Visualization and Computer Graphics, IEEE Transactions on, 20(12), 2003-2012.[6] Rind, A., Lammarsch, T., Aigner, W., Alsallakh, B. and Miksch, S. (2013). TimeBench: A Data Model and Software Library for Visual Analytics of Time-Oriented Data. IEEE Trans. Visual. Comput. Graphics, 19(12), pp.2247-2256.[7] Dou, W., Yu, L., Wang, X., Ma, Z., & Ribarsky, W. (2013). Hierarchicaltopics: Visually exploring large text collections using topic hierarchies. Visualization and Computer Graphics, IEEE Transactions on, 19(12), 2002-2011.[8] Liu, S., Wu, Y., Wei, E., Liu, M., & Liu, Y. (2013). Storyflow: Tracking the evolution of stories. Visualization and Computer Graphics, IEEE Transactions on, 19(12), 2436-2445.[9] Xu, P., Wu, Y., Wei, E., Peng, T. Q., Liu, S., Zhu, J. J., & Qu, H. (2013). Visual analysis of topic competition on social media. Visualization and Computer Graphics, IEEE Transactions on, 19(12), 2012-2021.[10] Stasko, J., & Zhang, E. (2000). Focus+ context display and navigation techniques for enhancing radial, space-filling hierarchy visualizations. InInformation Visualization, 2000. InfoVis 2000. IEEE Symposium on (pp. 57-65). IEEE.[11] Tufte, E. R. (2006). Beautiful evidence. New York.[12] Stasko, J., Catrambone, R., Guzdial, M., & McDonald, K. (2000). An evaluation of space-filling information visualizations for depicting hierarchical structures.International Journal of Human-Computer Studies, 53(5), 663-694.[13] Wang, X., Liu, S., Song, Y., & Guo, B. (2013, August). Mining evolutionary multi-branch trees from text streams. In Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 722-730). ACM.[14] Zhang, D., Zhai, C., Han, J., Srivastava, A., & Oza, N. (2009). Topic modeling for OLAP on multidimensional text databases: topic cube and its applications.Statistical Analysis and Data Mining: The ASA Data Science Journal, 2(5‐6), 378-395.[15] Blundell, C., Teh, Y. W., & Heller, K. A. (2012). Bayesian rose trees. arXiv preprint arXiv:1203.3468.[16] Liu, X., Song, Y., Liu, S., & Wang, H. (2012, August). Automatic taxonomy construction from keywords. In Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 1433-1441). ACM.[17] Wang, X., Liu, S., Song, Y., & Guo, B. (2013, August). Mining evolutionary multi-branch trees from text streams. In Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 722-730). ACM.[18] Cui, W., Liu, S., Wu, Z., & Wei, H. (2014). How hierarchical topics evolve in large text corpora. Visualization and Computer Graphics, IEEE Transactions on, 20(12), 2281-2290.[19] Furnas, G. W. (1986). Generalized fisheye views (Vol. 17, No. 4, pp. 16-23). ACM.[20] Li, Y., Sun, J., & Shum, H. Y. (2005). Video object cut and paste. ACM Transactions on Graphics (TOG), 24(3), 595-600.[21] Kriglstein, S., Pohl, M., & Stachl, C. (2012, July). Animation for time-oriented data: An overview of empirical research. In Information Visualisation (IV), 2012 16th International Conference on (pp. 30-35). IEEE[22] Johnson, B., & Shneiderman, B. (1991, October). Tree-maps: A space-filling approach to the visualization of hierarchical information structures. InVisualization, 1991. Visualization`91, Proceedings., IEEE Conference on (pp. 284-291). IEEE.[23] Wattenberg, M. (1999, May). Visualizing the stock market. In CHI`99 extended abstracts on Human factors in computing systems (pp. 188-189). ACM.[24] Van Wijk, J. J., & Van de Wetering, H. (1999). Cushion treemaps: Visualization of hierarchical information. In Information Visualization, 1999.(Info Vis` 99) Proceedings. 1999 IEEE Symposium on (pp. 73-78). IEEE.[25] Rubinstein, M., Gutierrez, D., Sorkine, O., & Shamir, A. (2010, December). A comparative study of image retargeting. In ACM transactions on graphics (TOG)(Vol. 29, No. 6, p. 160). ACM. zh_TW
