社群媒體是現代社會中獲取資訊的主要管道之一，同時也是人們發表意見的地方，然而，人們的意見經常被社群媒體上的意見領袖所左右，而意見領袖的聲量通常透過發表文章及評論來累積，但也有可能是特定集團抬轎而來，因此，本研究提出一個視覺化分析系統，將使用者之間的行為相似度表示成距離矩陣並排序，再以基於熱度圖的視覺化呈現，並且利用使用者與文章的二分關係將其繪製成具關聯矩陣風格的視覺化，結合上述兩者視覺化來觀察使用者活動情形並比較不同使用者類型的差異，可以幫助人們了解意見領袖的聲量是如何累積，以及他們是如何讓其關注的議題被更多人看見，最後，我們會做使用者研究來評估本論文的成效。

Social media is one of the main channels for obtaining information in modern society, and it is also a place where people can express their opinions. However, people's views are often influenced by opinion leaders on social media. The influence of opinion leaders is usually accumulated by publishing articles and comments, but it may also be carried by specific groups. Therefore, this research proposes a visual analysis system, which expresses the behavior similarity between users as a sorted distance matrix, and then visualizes them based on an ordered heatmap. We also use bipartite relations between users and articles to draw it into visualization with an adjacency matrix style. By combining the above two visualizations, we can observe users' activities and compare the differences between different types of users. The system can help people understand how to accumulate the influence of the opinion leader and how they make their concerns more visible.

第一章 緒論 1
1.1 研究動機與目的 1
1.2 問題描述 2
1.3 論文貢獻 2
第二章 相關研究 3
2.1 社群網路視覺化 3
2.2 文本資料視覺化 5
2.3 資訊傳播視覺化 6
2.4 二分圖視覺化 7
第三章 設計需求 9
第四章 研究方法與步驟 10
4.1 資料撈取和前處理 11
4.2 影響力計算 13
4.3 社群偵測 14
4.4 距離矩陣(distance matrix)與排序 16
4.5 視覺化設計 18
第五章 實驗結果與討論 31
5.1 使用案例 31
5.1.1 觀察使用者的抬轎行為 31
5.1.2 觀察使用者行為的改變 38
5.2 使用者研究 42
第六章 結論與未來發展 45
參考文獻 47

[1] J. Heer, & D. Boyd. Vizster: visualizing online social networks, IEEE Symposium on Information Visualization (INFOVIS 2005), 2005.
[2] Nan Cao, Yu-Ru Lin, Fan Du, & Dashun Wang. Episogram: Visual Summarization of Egocentric Social Interactions. IEEE Computer Graphics and Applications 36.5: 72-81, 2016.
[3] Mengdie Hu, Krist Wongsuphasawat, & John Stasko. Visualizing Social Media Content with SentenTree. IEEE Transactions on Visualization and Computer Graphics, 23.1, 2017.
[4] P. Xu, Y. Wu, E. Wei, T.-Q. Peng, S. Liu, J. J. H. Zhu, & H. Qu. Visual analysis of topic competition on social media. IEEE Transactions on Visualization and Computer Graphics, 19.12:2012–2021, 2013.
[5] F. Viégas, M. Wattenberg, & J. Hebert. Google+Ripples: a native visualization of information flow. WWW '13: Proceedings of the 22nd international conference on World Wide Web, pages 1389-1398, 2013.
[6] S. Chen, S. Chen, Z. Wang, J. Liang, X. Yuan, N. Cao, & Y. Wu. D-Map: Visual Analysis of Ego-centric Information Diffusion Patterns in Social Media. 2016 IEEE Conference on Visual Analytics Science and Technology (VAST), 2016.
[7] M. Sun, P. Mi, C. North, & N. Ramakrishnan. Biset: Semantic edge bundling with biclusters for sensemaking. IEEE transactions on visualization and computer graphics, 22(1):310–319, 2016.
[8] G. Y.-Y. Chan, P. Xu, Z. Dai, & L. Ren. Vibr: Visualizing bipartite relations at scale with the minimum description length principle. IEEE TVCG, 25(1):321–330, 2019.
[9] M. Bostock, V. Ogievetsky, & J. Heer. D3 data-driven documents, IEEE transactions on visualization and computer graphics, 17.12: 2301-2309, 2011.
[10] ptt-web-crawler is a crawler for the web version of ptt @ONLINE. [Online]. Available: https://github.com/jwlin/ptt-web-crawler.
[11] S. Tilkov & S. Vinoski, "Node. js: Using JavaScript to build high-performance network programs," IEEE Internet Computing, vol. 14, no. 6, pp. 80-83, 2010.
[12] Maoran Zhu, Xingkai Lin, Ting Lu, & Hongwei Wang. Identification of Opinion Leaders in Social Networks Based on Sentiment Analysis: Evidence from an Automotive Forum. International Conference on Modeling, Simulation and Optimization Technologies and Applications (MSOTA2016).
[13] M. Behrisch, B. Bach, N. Henry Riche, T. Schreck, & J.-D. Fekete. Matrix reordering methods for table and network visualization. Computer Graphics Forum, 35(3):693–716, 2016.
[14] FEKETE J.-D.. Reorder.js: A JavaScript Library to Reorder Tables and Networks. IEEE VIS 2015, Oct. 2015. Poster.
[15] V.D. Blondel, J.-L. Guillaume, & R. Lambiotte, and E. Lefebvre. Fast unfolding of communities in large networks. J. of Statistical Mechanics, page P10008, 2008.
[16] M. Newman. Modularity and community structure in networks. Proceedings of the National Academy of Sciences, 103(23):8577–8582, 2006.