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題名 以視覺化系統尋找社群媒體中的意見領袖
Design of a Visualization System for Finding Opinion Leaders in Social Media
作者 楊恩加
Yang, En-Jea
貢獻者 李蔡彥
Li, Tsai-Yen
楊恩加
Yang, En-Jea
關鍵詞 視覺化
意見領袖
推特
社群媒體
Visualization
Opinion leader
Twitter
Social media
日期 2019
上傳時間 12-Feb-2019 15:47:04 (UTC+8)
摘要 意見領袖（Opinion Leader），是人類社群在討論議題時自然形成的特色人物。從過往研究中，我們得知他們能有效並快速地將新消息傳達給大眾，並引導大眾的思考方向和結論。故此，我們想要深入地探討新聞事件的傳遞模式和樣貌，找出其中的意見領袖，和他們與大眾的連結關係。過往在人類行為學的領域上，已大致分出判斷意見領袖的十種方式。然而若直接套用在社群媒體中，則會因其數位化型態，互動方式的不同設定而無法切合應用。在本研究中，我們以在推特（Twitter）蒐集的三個主題事件（2015復興航空空難、2015亞洲投資銀行、2016台灣總統選舉）進行測試。藉由視覺化圖表、數據統計幫助研究者了解此主題事件的資料樣貌，再藉由數種找出意見領袖的方法，列舉出不同的候選人，並將其定位成重要連結者，以供研究者分析了解。為驗證本系統的可用性，我們邀請了五位受試者，透過操作教學、引導式任務讓受試者學習如何使用系統，接著讓受試者擇一事件找出三位以上的意見領袖，並透過問卷與訪談來探討系統的優缺點。實驗結果照示，系統在推特事件中能輔助研究者找出意見領袖。透過其視覺化輔助和資訊彙整，使用者能快速找到意見領袖，並以其在事件中與大眾的互動而更認識他們。
Opinion leaders usually are formed naturally when people discuss issues. From the literature, we know that they can convey information to the public quickly, and lead the people to think and draw conclusions. Therefore, in this research we would like to explore how news events propagate in social media in order to find the opinion leaders and understand their relationship between the people. In the literature, there are ten ways to distinguish opinion leaders through the study of human behaviors. However, not all of them can be applied to social media directly because of the differences between real world and social media.
In our research, we have used three datasets collected from Twitter as case studies. The topics of these datasets include TransAsia Airways Flight 235, Asian Infrastructure Investment Bank and 2016 Taiwan presidential election. We have developed a visual analysis system to help researchers grasp the datasets in a broad sense, and then find candidates for opinion leaders by filtering the data with several indexes. The opinion leaders are then identified by further information such as the social network relations. To verify the usefulness of our system, we have invited five subjects to participate in a series of tasks to find three opinion leaders in a given event. The subjects are asked to evaluate the system and give textual feedbacks about their experiences. The experimental results reveal that through the visualization functions and data consolidation, this system can effectively help researchers find opinion leaders and know their roles in social media.
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[3] D. A. b. Marzuki, "Traditional Media Vs New Media," 2012.
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[5] W. contributors. "Two-step flow of communication," 7 December 2018; https://en.wikipedia.org/w/index.php?title=Two-step_flow_of_communication&oldid=849913478.
[6] T. Economist. "All too much," http://www.economist.com/node/15557421.
[7] B. Marr. "Big Data: The 5 Vs Everyone Must Know," https://www.linkedin.com/pulse/20140306073407-64875646-big-data-the-5-vs-everyone-must-know.
[8] 鄭宇君, and 陳百齡, “探索2012年台灣總統大選之社交媒體浮現社群：鉅量資料分析取徑,” 新聞學研究, no. 120, pp. 121-166, 2014.
[9] W. contributors. "TransAsia Airways Flight 235," 7 December 2018; https://en.wikipedia.org/w/index.php?title=TransAsia_Airways_Flight_235&oldid=869568068.
[10] W. contributors. "Asian Infrastructure Investment Bank," 7 December 2018; https://en.wikipedia.org/w/index.php?title=Asian_Infrastructure_Investment_Bank&oldid=871317157.
[11] W. contributors. "Taiwan general election, 2016," 7 December 2018; https://en.wikipedia.org/w/index.php?title=Taiwan_general_election,_2016&oldid=872294746.
[12] R. K. Merton, Social theory and social structure: Simon and Schuster, 1968.
[13] S. KEMP. "DIGITAL IN 2018," https://wearesocial.com/blog/2018/01/global-digital-report-2018.
[14] E. Katz, “The Two-Step Flow of Communication: An Up-To-Date Report on an Hypothesis*,” Public Opinion Quarterly, vol. 21, no. 1, pp. 61-78, 1957.
[15] E. Katz, P. F. Lazarsfeld, and E. Roper, Personal Influence, New York: Routledge, 2005.
[16] R. S. Burt, “The social capital of opinion leaders,” The Annals of the American Academy of Political and Social Science, vol. 566, no. 1, pp. 37-54, 1999.
[17] T. W. Valente, and R. L. Davis, “Accelerating the diffusion of innovations using opinion leaders,” The Annals of the American Academy of Political and Social Science, vol. 566, no. 1, pp. 55-67, 1999.
[18] G. Weimann, “The influentials: back to the concept of opinion leaders?,” Public Opinion Quarterly, vol. 55, no. 2, pp. 267-279, 1991.
[19] T. W. Valente, and P. Pumpuang, “Identifying opinion leaders to promote behavior change,” Health Education & Behavior, vol. 34, no. 6, pp. 881-896, 2007.
[20] A. Mislove, M. Marcon, K. P. Gummadi, P. Druschel, and B. Bhattacharjee, “Measurement and analysis of online social networks,” in Proceedings of the 7th ACM SIGCOMM conference on Internet measurement, San Diego, California, USA, 2007, pp. 29-42.
[21] W. contributors. "Power law," 7 December 2018; https://en.wikipedia.org/w/index.php?title=Power_law&oldid=871827440.
[22] W. contributors. "Small-world network," 7 December 2018; https://en.wikipedia.org/w/index.php?title=Small-world_network&oldid=869336993.
[23] W. contributors. "Clustering coefficient," 7 December 2018; https://en.wikipedia.org/w/index.php?title=Clustering_coefficient&oldid=863532766.
[24] W. contributors. "Scale-free network," 7 December 2018; https://en.wikipedia.org/w/index.php?title=Scale-free_network&oldid=869554385.
[25] A. M. Kaplan, and M. Haenlein, “Users of the world, unite! The challenges and opportunities of Social Media,” Business Horizons, vol. 53, no. 1, pp. 59-68, 2010/01/01/, 2010.
[26] M. Cha, H. Haddadi, F. Benevenuto, and P. K. Gummadi, “Measuring user influence in twitter: The million follower fallacy,” The International AAAI Conference on Web and Social Media, vol. 10, no. 10-17, pp. 30, 2010.
[27] E. Dubois, and D. Gaffney, “The multiple facets of influence: identifying political influentials and opinion leaders on Twitter,” American Behavioral Scientist, vol. 58, no. 10, pp. 1260-1277, 2014.
[28] V. R. Embar, I. Bhattacharya, V. Pandit, and R. Vaculin, “Online Topic-based Social Influence Analysis for the Wimbledon Championships,” in Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Sydney, NSW, Australia, 2015, pp. 1759-1768.
[29] E. Bakshy, J. M. Hofman, W. A. Mason, and D. J. Watts, “Everyone`s an influencer: quantifying influence on twitter,” in Proceedings of the fourth ACM international conference on Web search and data mining, Hong Kong, China, 2011, pp. 65-74.
[30] M. O. Ward, G. Grinstein, and D. Keim, Interactive data visualization: foundations, techniques, and applications: AK Peters/CRC Press, 2015.
[31] C. D. Hansen, and C. R. Johnson, Visualization handbook: Elsevier, 2011.
[32] InternetLiveStats. "Twitter Usage Statistics," 7 December 2018; http://www.internetlivestats.com/twitter-statistics/.
[33] E. Borra, and B. Rieder, “Programmed method: developing a toolset for capturing and analyzing tweets,” Aslib Journal of Information Management, vol. 66, no. 3, pp. 262-278, 2014.
[34] P. Xu, Y. Wu, E. Wei, T.-Q. Peng, S. Liu, J. J. Zhu, and H. Qu, “Visual analysis of topic competition on social media,” IEEE Transactions on Visualization and Computer Graphics, vol. 19, no. 12, pp. 2012-2021, 2013.
[35] N. Cao, Y.-R. Lin, X. Sun, D. Lazer, S. Liu, and H. Qu, “Whisper: Tracing the spatiotemporal process of information diffusion in real time,” IEEE Transactions on Visualization and Computer Graphics, vol. 18, no. 12, pp. 2649-2658, 2012.
[36] 林聖翔, “新聞事件於社群媒體之發展歷程的視覺化分析工具: 以 Twitter 為例,” 政治大學, 2017.
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描述 碩士
國立政治大學
資訊科學系
104753028
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0104753028
資料類型 thesis
dc.contributor.advisor 李蔡彥zh_TW
dc.contributor.advisor Li, Tsai-Yenen_US
dc.contributor.author (Authors) 楊恩加zh_TW
dc.contributor.author (Authors) Yang, En-Jeaen_US
dc.creator (作者) 楊恩加zh_TW
dc.creator (作者) Yang, En-Jeaen_US
dc.date (日期) 2019en_US
dc.date.accessioned 12-Feb-2019 15:47:04 (UTC+8)-
dc.date.available 12-Feb-2019 15:47:04 (UTC+8)-
dc.date.issued (上傳時間) 12-Feb-2019 15:47:04 (UTC+8)-
dc.identifier (Other Identifiers) G0104753028en_US
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/122285-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 資訊科學系zh_TW
dc.description (描述) 104753028zh_TW
dc.description.abstract (摘要) 意見領袖（Opinion Leader），是人類社群在討論議題時自然形成的特色人物。從過往研究中，我們得知他們能有效並快速地將新消息傳達給大眾，並引導大眾的思考方向和結論。故此，我們想要深入地探討新聞事件的傳遞模式和樣貌，找出其中的意見領袖，和他們與大眾的連結關係。過往在人類行為學的領域上，已大致分出判斷意見領袖的十種方式。然而若直接套用在社群媒體中，則會因其數位化型態，互動方式的不同設定而無法切合應用。在本研究中，我們以在推特（Twitter）蒐集的三個主題事件（2015復興航空空難、2015亞洲投資銀行、2016台灣總統選舉）進行測試。藉由視覺化圖表、數據統計幫助研究者了解此主題事件的資料樣貌，再藉由數種找出意見領袖的方法，列舉出不同的候選人，並將其定位成重要連結者，以供研究者分析了解。為驗證本系統的可用性，我們邀請了五位受試者，透過操作教學、引導式任務讓受試者學習如何使用系統，接著讓受試者擇一事件找出三位以上的意見領袖，並透過問卷與訪談來探討系統的優缺點。實驗結果照示，系統在推特事件中能輔助研究者找出意見領袖。透過其視覺化輔助和資訊彙整，使用者能快速找到意見領袖，並以其在事件中與大眾的互動而更認識他們。zh_TW
dc.description.abstract (摘要) Opinion leaders usually are formed naturally when people discuss issues. From the literature, we know that they can convey information to the public quickly, and lead the people to think and draw conclusions. Therefore, in this research we would like to explore how news events propagate in social media in order to find the opinion leaders and understand their relationship between the people. In the literature, there are ten ways to distinguish opinion leaders through the study of human behaviors. However, not all of them can be applied to social media directly because of the differences between real world and social media.
In our research, we have used three datasets collected from Twitter as case studies. The topics of these datasets include TransAsia Airways Flight 235, Asian Infrastructure Investment Bank and 2016 Taiwan presidential election. We have developed a visual analysis system to help researchers grasp the datasets in a broad sense, and then find candidates for opinion leaders by filtering the data with several indexes. The opinion leaders are then identified by further information such as the social network relations. To verify the usefulness of our system, we have invited five subjects to participate in a series of tasks to find three opinion leaders in a given event. The subjects are asked to evaluate the system and give textual feedbacks about their experiences. The experimental results reveal that through the visualization functions and data consolidation, this system can effectively help researchers find opinion leaders and know their roles in social media.		en_US
dc.description.tableofcontents 摘要 i
Abstract ii
致謝 iii
圖目錄 vi
表目錄 vii
第 1 章 導論 1
1.1 研究動機 1
1.2 研究目標 5
1.3 研究問題 6
1.4 論文貢獻 7
1.5 論文架構 8
第 2 章 相關研究 9
2.1 現實世界中找尋意見領袖 9
2.2 在推特中找尋意見領袖 11
2.3 資訊視覺化 15
第 3 章 系統架構與設計 19
3.1. 系統架構 19
3.1. 資料來源 20
3.1.1. 推特使用者發文 20
3.1.2. 推特使用者檔案 22
3.1.3. Website 22
3.2. 系統介面設計 23
3.2.1. 前導介紹與登入 24
3.2.2. 資料集分析區 26
3.2.3. 以個別指標找出領袖區 27
3.2.4. 以綜合指標選出領袖區 29
3.2.5. 結果展示區 32
第 4 章 系統實作 33
4.1 資料收集 33
4.2 響應式網頁設計 34
4.3 視覺化呈現 37
4.4 範例說明 40
第 5 章 實驗設計與結果分析 49
5.1 實驗目標 49
5.2 實驗對象 49
5.3 實驗流程 50
5.3.1 引導式任務 51
5.3.2 指定任務 51
5.3.3 問卷與訪談 52
5.4 實驗結果分析與討論 56
5.4.1 有用性評估 56
5.4.2 易用性評估 60
第 6 章 結論與未來展望 61
6.1 研究結論 61
6.2 未來發展與改進 62
參考文獻 63
附錄 67
附錄1　引導式任務內容設計 67		zh_TW
dc.format.extent 2921839 bytes-
dc.format.mimetype application/pdf-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0104753028en_US
dc.subject (關鍵詞) 視覺化zh_TW
dc.subject (關鍵詞) 意見領袖zh_TW
dc.subject (關鍵詞) 推特zh_TW
dc.subject (關鍵詞) 社群媒體zh_TW
dc.subject (關鍵詞) Visualizationen_US
dc.subject (關鍵詞) Opinion leaderen_US
dc.subject (關鍵詞) Twitteren_US
dc.subject (關鍵詞) Social mediaen_US
dc.title (題名) 以視覺化系統尋找社群媒體中的意見領袖zh_TW
dc.title (題名) Design of a Visualization System for Finding Opinion Leaders in Social Mediaen_US
dc.type (資料類型) thesisen_US
dc.relation.reference (參考文獻) [1] W. contributors. "Web 2.0," 7 December 2018; https://en.wikipedia.org/w/index.php?title=Web_2.0&oldid=871056182.
[2] 1mayamaria, "Traditional Media Vs New Media," 2015.
[3] D. A. b. Marzuki, "Traditional Media Vs New Media," 2012.
[4] 食來運轉. "傳統媒體VS自媒體？," 7 December 2018; https://kknews.cc/zh-tw/other/o89pgq.html.
[5] W. contributors. "Two-step flow of communication," 7 December 2018; https://en.wikipedia.org/w/index.php?title=Two-step_flow_of_communication&oldid=849913478.
[6] T. Economist. "All too much," http://www.economist.com/node/15557421.
[7] B. Marr. "Big Data: The 5 Vs Everyone Must Know," https://www.linkedin.com/pulse/20140306073407-64875646-big-data-the-5-vs-everyone-must-know.
[8] 鄭宇君, and 陳百齡, “探索2012年台灣總統大選之社交媒體浮現社群：鉅量資料分析取徑,” 新聞學研究, no. 120, pp. 121-166, 2014.
[9] W. contributors. "TransAsia Airways Flight 235," 7 December 2018; https://en.wikipedia.org/w/index.php?title=TransAsia_Airways_Flight_235&oldid=869568068.
[10] W. contributors. "Asian Infrastructure Investment Bank," 7 December 2018; https://en.wikipedia.org/w/index.php?title=Asian_Infrastructure_Investment_Bank&oldid=871317157.
[11] W. contributors. "Taiwan general election, 2016," 7 December 2018; https://en.wikipedia.org/w/index.php?title=Taiwan_general_election,_2016&oldid=872294746.
[12] R. K. Merton, Social theory and social structure: Simon and Schuster, 1968.
[13] S. KEMP. "DIGITAL IN 2018," https://wearesocial.com/blog/2018/01/global-digital-report-2018.
[14] E. Katz, “The Two-Step Flow of Communication: An Up-To-Date Report on an Hypothesis*,” Public Opinion Quarterly, vol. 21, no. 1, pp. 61-78, 1957.
[15] E. Katz, P. F. Lazarsfeld, and E. Roper, Personal Influence, New York: Routledge, 2005.
[16] R. S. Burt, “The social capital of opinion leaders,” The Annals of the American Academy of Political and Social Science, vol. 566, no. 1, pp. 37-54, 1999.
[17] T. W. Valente, and R. L. Davis, “Accelerating the diffusion of innovations using opinion leaders,” The Annals of the American Academy of Political and Social Science, vol. 566, no. 1, pp. 55-67, 1999.
[18] G. Weimann, “The influentials: back to the concept of opinion leaders?,” Public Opinion Quarterly, vol. 55, no. 2, pp. 267-279, 1991.
[19] T. W. Valente, and P. Pumpuang, “Identifying opinion leaders to promote behavior change,” Health Education & Behavior, vol. 34, no. 6, pp. 881-896, 2007.
[20] A. Mislove, M. Marcon, K. P. Gummadi, P. Druschel, and B. Bhattacharjee, “Measurement and analysis of online social networks,” in Proceedings of the 7th ACM SIGCOMM conference on Internet measurement, San Diego, California, USA, 2007, pp. 29-42.
[21] W. contributors. "Power law," 7 December 2018; https://en.wikipedia.org/w/index.php?title=Power_law&oldid=871827440.
[22] W. contributors. "Small-world network," 7 December 2018; https://en.wikipedia.org/w/index.php?title=Small-world_network&oldid=869336993.
[23] W. contributors. "Clustering coefficient," 7 December 2018; https://en.wikipedia.org/w/index.php?title=Clustering_coefficient&oldid=863532766.
[24] W. contributors. "Scale-free network," 7 December 2018; https://en.wikipedia.org/w/index.php?title=Scale-free_network&oldid=869554385.
[25] A. M. Kaplan, and M. Haenlein, “Users of the world, unite! The challenges and opportunities of Social Media,” Business Horizons, vol. 53, no. 1, pp. 59-68, 2010/01/01/, 2010.
[26] M. Cha, H. Haddadi, F. Benevenuto, and P. K. Gummadi, “Measuring user influence in twitter: The million follower fallacy,” The International AAAI Conference on Web and Social Media, vol. 10, no. 10-17, pp. 30, 2010.
[27] E. Dubois, and D. Gaffney, “The multiple facets of influence: identifying political influentials and opinion leaders on Twitter,” American Behavioral Scientist, vol. 58, no. 10, pp. 1260-1277, 2014.
[28] V. R. Embar, I. Bhattacharya, V. Pandit, and R. Vaculin, “Online Topic-based Social Influence Analysis for the Wimbledon Championships,” in Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Sydney, NSW, Australia, 2015, pp. 1759-1768.
[29] E. Bakshy, J. M. Hofman, W. A. Mason, and D. J. Watts, “Everyone`s an influencer: quantifying influence on twitter,” in Proceedings of the fourth ACM international conference on Web search and data mining, Hong Kong, China, 2011, pp. 65-74.
[30] M. O. Ward, G. Grinstein, and D. Keim, Interactive data visualization: foundations, techniques, and applications: AK Peters/CRC Press, 2015.
[31] C. D. Hansen, and C. R. Johnson, Visualization handbook: Elsevier, 2011.
[32] InternetLiveStats. "Twitter Usage Statistics," 7 December 2018; http://www.internetlivestats.com/twitter-statistics/.
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dc.identifier.doi (DOI) 10.6814/THE.NCCU.CS.005.2019.B02en_US