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題名 以社群媒體輔助新聞主題探索的視覺化資訊系統
A Visualization Information System to Assist News Topics Exploration with Social Media
作者 林靖雅
Lin, Ching Ya
貢獻者 李蔡彥
Li, Tsai Yen
林靖雅
Lin, Ching Ya
關鍵詞 社群媒體
新聞主題
推特
視覺化
social media
news topics
twitter
visualization
日期 2015
上傳時間 2-Nov-2015 14:50:29 (UTC+8)
摘要 隨著社群媒體的普及，群眾產製的內容（User-generated content, UGC）時常成為新聞記者取材的對象，但現今隨著社群媒體爆發的資料量，記者不易從資料中看到事件的全貌，僅將社群媒體當作一種消息來源，因此報導的內容經常抄襲網友的意見或是落入片面討論的窠臼，無法駕馭社群媒體帶來的豐富資料。考慮改善這樣的現象，本研究透過將新聞取材的過程分為探索事件、收集素材以及回溯情境三個動作來協助記者探索新聞主題。以推特（Twitter）的資料為例，以網路為系統平台，開發一個輔助記者探索社群媒體上的事件、挖掘新聞主題的資訊系統，利用網絡分析以及自然語言處理的技術，結合視覺化的介面將事件資料集用故事元素的方式呈現，四種故事元素模型提供不同的觀察資料集的角度，並利用調整四種故事元素的權重，還原推文文本的語境，找出使用者想看的內容。我們設計了兩階段的任務式實驗以及評估問卷來證明系統的可用性，透過實驗結果驗證了本研究在以社群媒體輔助記者探索新聞主題的系統之價值，能讓對事件不同熟悉程度的傳播記者在此平台上探索新聞主題，並寫下深度報導的編採線索或是一篇新聞報導，透過本系統的輔助，讓使用者在探索及追蹤一起事件時，變得較為快速。
With the popularity of social media, news reporters usually draw the news materials from mass user-generated content. However, with the outbreak of social media data, the reporter is not easy to see from the data in the whole picture of event. They only use the social media as a news source, so the reported content often copied the views of users, or fall into the stereotype of a one-sided discussion. The reporters can not control the wealth of information brought from social media. Consider improving this phenomenon, our study use Twitter data for example, develop an information system to assist reporters to explore the events on social media, and mine the news topics. We use network analysis and natural language processing as our technique, and show the story elements with the visualization interface. We apply four different story elements model, support the different way to explore data, and let user can adjust the weights from different model to retrospect to the context of tweets, help user find the news topics. We have designed a two-stage task experiment and assessment questionnaire to prove the availability of the system through experimental results. We can allow the reporters who are varying degrees of familiarity of the event to explore news topics from our system. We make the reporter to explore and track some events faster.
參考文獻 [1] D. M. White, “The ‘Gate Keeper’: A Case Study in the Selection of News,” Journal. Q., vol. 27, pp. 383–390, 1950.
[2] A. Bruns, “Gatewatching, Not Gatekeeping: Collaborative Online News,” Media Int. Aust., no. 107, pp. 31–44, 2003.
[3] L. Willnat and D. Weaver, “The American Journalist in the Digital Age,” 2013.
[4] J. Harrison, “User-Generated Content and Gatekeeping at The BBC Hub,” Journalism Studies, vol. 11, no. 2. pp. 243–256, 2010.
[5] E. Siapera, “From couch potatoes to cybernauts? The expanding notion of the audience on TV channels’ websites,” New Media Soc., vol. 6, no. 2, pp. 155–172, Apr. 2004.
[6] “Twitonomy.” [Online]. Available: http://www.twitonomy.com/. [Accessed: 15-Oct-2015].
[7] “Twitter Collection and Analysis Toolkit (TCAT),” BETWEETNESS LAB. [Online]. Available: http://www.betweetness.com/. [Accessed: 03-Oct-2014].
[8] “Dataminr.” [Online]. Available: https://www.dataminr.com/. [Accessed: 03-Dec-2014].
[9] A. Zubiaga, H. Ji, and K. Knight, “Curating and Contextualizing Twitter Stories to Assist with Social Newsgathering,” Proc. 2013 Int. Conf. Intell. user interfaces - IUI ’13, p. 213, 2013.
[10] E. Borra and B. Rieder, “Programmed method: developing a toolset for capturing and analyzing tweets,” Aslib J. Inf. Manag., vol. 66, no. 3, pp. 262–278, May 2014.
[11] A. Marcus, M. S. Bernstein, O. Badar, D. R. Karger, S. Madden, and R. C. Miller, “Twitinfo: Aggregating and Visualizing Microblogs for Event Exploration.,” in Proceedings of the 2011 Annual Conference on Human Factors in Computing Systems, 2011, pp. 227–236.
[12] N. A. Diakopoulos and D. A. Shamma, “Characterizing Debate Performance via Aggregated Twitter Sentiment.,” in the 28th International Conference on Human Factors in Computing Systems, 2010, pp. 1195–1198.
[13] N. Diakopoulos, M. de Choudhury, and M. Naaman, “Finding and Assessing Social Media Information Sources in the Context of Journalism.,” in he 2012 ACM Annual Conference on Human Factors in Computing Systems, CHI ’12, 2012, pp. 2451–2460.
[14] H. Becker, M. Naaman, and L. Gravano, “Beyond Trending Topics: Real-World Event Identification on Twitter,” in the Fifth International AAAI Conference on Weblogs and Social Media, 2011.
[15] 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, 2010, vol. 3, pp. 120–123.
[16] M. Mathioudakis and N. Koudas, “TwitterMonitor: Trend Detection over the Twitter Stream,” in Proceedings of the 2010 ACM SIGMOD International Conference on Management of data, 2010.
[17] S. Asur, B. A. Huberman, G. Szabo, and C. Wang, “Trends in social media: Persistence and decay.,” in 5th International AAAI Conference on Weblogs and Social Media, 2011.
[18] M. Cheong and V. Lee, “Integrating Web-based Intelligence Retrieval and Decision-making from the Twitter Trends Knowledge Base,” in the 2nd ACM Workshop on Social Web Search and Mining, 2009.
[19] A. Zubiaga, D. Spina, V. Fresno, and R. Martínez, “Classifying Trending Topics : A Typology of Conversation Triggers on Twitter,” Proc. 20th ACM Int. Conf. Inf. Knowl. Manag., pp. 8–11, 2011.
[20] “Twitter Streaming APIs.” [Online]. Available: https://dev.twitter.com/streaming/overview. [Accessed: 08-Oct-2015].
[21] “Twitter Search APIs.” [Online]. Available: https://dev.twitter.com/rest/reference/get/search/tweets. [Accessed: 08-Oct-2015].
[22] “讓愛與和平佔領中環,” 維基百科, 2014. [Online]. Available: https://zh.wikipedia.org/wiki/%E8%AE%93%E6%84%9B%E8%88%87%E5%92%8C%E5%B9%B3%E4%BD%94%E9%A0%98%E4%B8%AD%E7%92%B0. [Accessed: 15-Oct-2015].
[23] A. Bruns and S. Stieglitz, “Towards more systematic Twitter analysis: metrics for tweeting activities,” Int. J. Soc. Res. Methodol., vol. 16, no. 2, pp. 91–108, 2013.
[24] “Betweenness Centrality.” [Online]. Available: https://en.wikipedia.org/wiki/Betweenness_centrality. [Accessed: 15-Oct-2015].
[25] “中文斷詞系統,” Taiwan National Digital Archives Program. [Online]. Available: http://ckipsvr.iis.sinica.edu.tw/. [Accessed: 15-Oct-2015].
[26] fxsjy, “结巴中文分词.” [Online]. Available: https://github.com/fxsjy/jieba. [Accessed: 15-Oct-2015].
[27] D. M. Blei, A. Y. Ng, and M. I. Jordan, “Latent dirichlet allocation,” J. Mach. Learn. Res., vol. 3, pp. 993–1022, 2003.
[28] X. Yan, J. Guo, Y. Lan, and X. Cheng, “A biterm topic model for short texts,” WWW ’13 Proc. 22nd Int. Conf. World Wide Web, pp. 1445–1456, 2013.
[29] “SIGMA.js: an open-source lightweight javaScript library.” [Online]. Available: http://sigmajs.org/.
[30] V. D. Blondel, J.-L. Guillaume, R. Lambiotte, and E. Lefebvre, “Fast unfolding of communities in large networks,” J. Stat. Mech. Theory Exp., vol. 2008, no. 10, p. P10008, 2008.
[31] M. Jacomy, S. Heymann, T. Venturini, and M. Bastian, “Forceatlas2, a continuous graph layout algorithm for handy network visualization,” Medialab Cent. Res., vol. 560, 2011.
[32] T. M. J. Fruchterman and E. M. Reingold, “Graph Drawing by Force-directed Placement,” Software-Practice Exp., vol. 21, no. November, pp. 1129–1164, 1991.
[33] J. Brooke, “SUS - A quick and dirty usability scale,” Usability Eval. Ind., vol. 189, no. 194, pp. 4–7, 1996.
[34] T. Tullis and W. Albert, Measuring the User Experience: Collecting, Analyzing, and Presenting Usability Metrics. San Francisco, CA, USA: Morgan Kaufmann Publishers Inc., 2008.
[35] K. Finstad, “The System Usability Scale and Non-Native English Speakers,” English, vol. 1, no. 4, pp. 185–188, 2006.
[36] A. Bangor, “Determining What Individual SUS Scores Mean: Adding an Adjective Rating Scale,” J. usability Stud., vol. 4, no. 3, pp. 114–123, 2009.
[37] “Gephi,” Wikipedia. [Online]. Available: https://en.wikipedia.org/wiki/Gephi. [Accessed: 14-Oct-2015].
描述 碩士
國立政治大學
資訊科學學系
102753002
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0102753002
資料類型 thesis
dc.contributor.advisor 李蔡彥zh_TW
dc.contributor.advisor Li, Tsai Yenen_US
dc.contributor.author (Authors) 林靖雅zh_TW
dc.contributor.author (Authors) Lin, Ching Yaen_US
dc.creator (作者) 林靖雅zh_TW
dc.creator (作者) Lin, Ching Yaen_US
dc.date (日期) 2015en_US
dc.date.accessioned 2-Nov-2015 14:50:29 (UTC+8)-
dc.date.available 2-Nov-2015 14:50:29 (UTC+8)-
dc.date.issued (上傳時間) 2-Nov-2015 14:50:29 (UTC+8)-
dc.identifier (Other Identifiers) G0102753002en_US
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/79207-
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) 102753002zh_TW
dc.description.abstract (摘要) 隨著社群媒體的普及，群眾產製的內容（User-generated content, UGC）時常成為新聞記者取材的對象，但現今隨著社群媒體爆發的資料量，記者不易從資料中看到事件的全貌，僅將社群媒體當作一種消息來源，因此報導的內容經常抄襲網友的意見或是落入片面討論的窠臼，無法駕馭社群媒體帶來的豐富資料。考慮改善這樣的現象，本研究透過將新聞取材的過程分為探索事件、收集素材以及回溯情境三個動作來協助記者探索新聞主題。以推特（Twitter）的資料為例，以網路為系統平台，開發一個輔助記者探索社群媒體上的事件、挖掘新聞主題的資訊系統，利用網絡分析以及自然語言處理的技術，結合視覺化的介面將事件資料集用故事元素的方式呈現，四種故事元素模型提供不同的觀察資料集的角度，並利用調整四種故事元素的權重，還原推文文本的語境，找出使用者想看的內容。我們設計了兩階段的任務式實驗以及評估問卷來證明系統的可用性，透過實驗結果驗證了本研究在以社群媒體輔助記者探索新聞主題的系統之價值，能讓對事件不同熟悉程度的傳播記者在此平台上探索新聞主題，並寫下深度報導的編採線索或是一篇新聞報導，透過本系統的輔助，讓使用者在探索及追蹤一起事件時，變得較為快速。zh_TW
dc.description.abstract (摘要) With the popularity of social media, news reporters usually draw the news materials from mass user-generated content. However, with the outbreak of social media data, the reporter is not easy to see from the data in the whole picture of event. They only use the social media as a news source, so the reported content often copied the views of users, or fall into the stereotype of a one-sided discussion. The reporters can not control the wealth of information brought from social media. Consider improving this phenomenon, our study use Twitter data for example, develop an information system to assist reporters to explore the events on social media, and mine the news topics. We use network analysis and natural language processing as our technique, and show the story elements with the visualization interface. We apply four different story elements model, support the different way to explore data, and let user can adjust the weights from different model to retrospect to the context of tweets, help user find the news topics. We have designed a two-stage task experiment and assessment questionnaire to prove the availability of the system through experimental results. We can allow the reporters who are varying degrees of familiarity of the event to explore news topics from our system. We make the reporter to explore and track some events faster.en_US
dc.description.tableofcontents 以社群媒體輔助新聞主題探索的視覺化資訊系統 1
摘要 i
Abstract ii
目錄 iii
圖目錄 vii
表目錄 x
第1章 導論 1
1.1 研究動機 1
1.2 研究目標 4
1.3 論文貢獻 5
第2章 相關研究 8
2.1 分析推特資料的工具 8
2.2 使用推特幫助新聞記者 9
第3章 系統概念與介面設計 12
3.1 系統概念 13
3.2 資料來源 14
3.3 系統介面設計 16
3.4 故事元素模型 21
3.5 推文素材收集、整理與歸類 34
第4章 系統實作技術 37
4.1 推特資料蒐集 37
4.2 推文斷詞與主題探勘 39
4.3 故事元素模型之視覺化技術 40
4.4 客製化決定推文順序 43
第5章 實驗設計與結果分析 47
5.1 實驗目標 47
5.2 實驗對象 48
5.3 實驗流程 49
5.3.1 引導式任務熟悉介面 49
5.3.2 第一階段問卷調查 50
5.3.3 指定任務 55
5.3.4 第二階段問卷調查 56
5.4 實驗結果分析與討論 58
5.4.1 系統易用性問卷內容分析 59
5.4.2 系統有用性問卷內容分析 61
5.4.3 系統可用性尺度量表（SUS）比較分析結果 64
5.5 開放性問題訪談結果 73
第6章 結論與未來展望 77
6.1 研究結論 77
6.2 未來發展與改進 78
參考文獻 80
附錄 84
附錄A 第一階段：引導式任務熟悉介面 84
附錄B 受測者開放性問題回饋 95		zh_TW
dc.format.extent 3925466 bytes-
dc.format.mimetype application/pdf-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0102753002en_US
dc.subject (關鍵詞) 社群媒體zh_TW
dc.subject (關鍵詞) 新聞主題zh_TW
dc.subject (關鍵詞) 推特zh_TW
dc.subject (關鍵詞) 視覺化zh_TW
dc.subject (關鍵詞) social mediaen_US
dc.subject (關鍵詞) news topicsen_US
dc.subject (關鍵詞) twitteren_US
dc.subject (關鍵詞) visualizationen_US
dc.title (題名) 以社群媒體輔助新聞主題探索的視覺化資訊系統zh_TW
dc.title (題名) A Visualization Information System to Assist News Topics Exploration with Social Mediaen_US
dc.type (資料類型) thesisen
dc.relation.reference (參考文獻) [1] D. M. White, “The ‘Gate Keeper’: A Case Study in the Selection of News,” Journal. Q., vol. 27, pp. 383–390, 1950.
[2] A. Bruns, “Gatewatching, Not Gatekeeping: Collaborative Online News,” Media Int. Aust., no. 107, pp. 31–44, 2003.
[3] L. Willnat and D. Weaver, “The American Journalist in the Digital Age,” 2013.
[4] J. Harrison, “User-Generated Content and Gatekeeping at The BBC Hub,” Journalism Studies, vol. 11, no. 2. pp. 243–256, 2010.
[5] E. Siapera, “From couch potatoes to cybernauts? The expanding notion of the audience on TV channels’ websites,” New Media Soc., vol. 6, no. 2, pp. 155–172, Apr. 2004.
[6] “Twitonomy.” [Online]. Available: http://www.twitonomy.com/. [Accessed: 15-Oct-2015].
[7] “Twitter Collection and Analysis Toolkit (TCAT),” BETWEETNESS LAB. [Online]. Available: http://www.betweetness.com/. [Accessed: 03-Oct-2014].
[8] “Dataminr.” [Online]. Available: https://www.dataminr.com/. [Accessed: 03-Dec-2014].
[9] A. Zubiaga, H. Ji, and K. Knight, “Curating and Contextualizing Twitter Stories to Assist with Social Newsgathering,” Proc. 2013 Int. Conf. Intell. user interfaces - IUI ’13, p. 213, 2013.
[10] E. Borra and B. Rieder, “Programmed method: developing a toolset for capturing and analyzing tweets,” Aslib J. Inf. Manag., vol. 66, no. 3, pp. 262–278, May 2014.
[11] A. Marcus, M. S. Bernstein, O. Badar, D. R. Karger, S. Madden, and R. C. Miller, “Twitinfo: Aggregating and Visualizing Microblogs for Event Exploration.,” in Proceedings of the 2011 Annual Conference on Human Factors in Computing Systems, 2011, pp. 227–236.
[12] N. A. Diakopoulos and D. A. Shamma, “Characterizing Debate Performance via Aggregated Twitter Sentiment.,” in the 28th International Conference on Human Factors in Computing Systems, 2010, pp. 1195–1198.
[13] N. Diakopoulos, M. de Choudhury, and M. Naaman, “Finding and Assessing Social Media Information Sources in the Context of Journalism.,” in he 2012 ACM Annual Conference on Human Factors in Computing Systems, CHI ’12, 2012, pp. 2451–2460.
[14] H. Becker, M. Naaman, and L. Gravano, “Beyond Trending Topics: Real-World Event Identification on Twitter,” in the Fifth International AAAI Conference on Weblogs and Social Media, 2011.
[15] 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, 2010, vol. 3, pp. 120–123.
[16] M. Mathioudakis and N. Koudas, “TwitterMonitor: Trend Detection over the Twitter Stream,” in Proceedings of the 2010 ACM SIGMOD International Conference on Management of data, 2010.
[17] S. Asur, B. A. Huberman, G. Szabo, and C. Wang, “Trends in social media: Persistence and decay.,” in 5th International AAAI Conference on Weblogs and Social Media, 2011.
[18] M. Cheong and V. Lee, “Integrating Web-based Intelligence Retrieval and Decision-making from the Twitter Trends Knowledge Base,” in the 2nd ACM Workshop on Social Web Search and Mining, 2009.
[19] A. Zubiaga, D. Spina, V. Fresno, and R. Martínez, “Classifying Trending Topics : A Typology of Conversation Triggers on Twitter,” Proc. 20th ACM Int. Conf. Inf. Knowl. Manag., pp. 8–11, 2011.
[20] “Twitter Streaming APIs.” [Online]. Available: https://dev.twitter.com/streaming/overview. [Accessed: 08-Oct-2015].
[21] “Twitter Search APIs.” [Online]. Available: https://dev.twitter.com/rest/reference/get/search/tweets. [Accessed: 08-Oct-2015].
[22] “讓愛與和平佔領中環,” 維基百科, 2014. [Online]. Available: https://zh.wikipedia.org/wiki/%E8%AE%93%E6%84%9B%E8%88%87%E5%92%8C%E5%B9%B3%E4%BD%94%E9%A0%98%E4%B8%AD%E7%92%B0. [Accessed: 15-Oct-2015].
[23] A. Bruns and S. Stieglitz, “Towards more systematic Twitter analysis: metrics for tweeting activities,” Int. J. Soc. Res. Methodol., vol. 16, no. 2, pp. 91–108, 2013.
[24] “Betweenness Centrality.” [Online]. Available: https://en.wikipedia.org/wiki/Betweenness_centrality. [Accessed: 15-Oct-2015].
[25] “中文斷詞系統,” Taiwan National Digital Archives Program. [Online]. Available: http://ckipsvr.iis.sinica.edu.tw/. [Accessed: 15-Oct-2015].
[26] fxsjy, “结巴中文分词.” [Online]. Available: https://github.com/fxsjy/jieba. [Accessed: 15-Oct-2015].
[27] D. M. Blei, A. Y. Ng, and M. I. Jordan, “Latent dirichlet allocation,” J. Mach. Learn. Res., vol. 3, pp. 993–1022, 2003.
[28] X. Yan, J. Guo, Y. Lan, and X. Cheng, “A biterm topic model for short texts,” WWW ’13 Proc. 22nd Int. Conf. World Wide Web, pp. 1445–1456, 2013.
[29] “SIGMA.js: an open-source lightweight javaScript library.” [Online]. Available: http://sigmajs.org/.
[30] V. D. Blondel, J.-L. Guillaume, R. Lambiotte, and E. Lefebvre, “Fast unfolding of communities in large networks,” J. Stat. Mech. Theory Exp., vol. 2008, no. 10, p. P10008, 2008.
[31] M. Jacomy, S. Heymann, T. Venturini, and M. Bastian, “Forceatlas2, a continuous graph layout algorithm for handy network visualization,” Medialab Cent. Res., vol. 560, 2011.
[32] T. M. J. Fruchterman and E. M. Reingold, “Graph Drawing by Force-directed Placement,” Software-Practice Exp., vol. 21, no. November, pp. 1129–1164, 1991.
[33] J. Brooke, “SUS - A quick and dirty usability scale,” Usability Eval. Ind., vol. 189, no. 194, pp. 4–7, 1996.
[34] T. Tullis and W. Albert, Measuring the User Experience: Collecting, Analyzing, and Presenting Usability Metrics. San Francisco, CA, USA: Morgan Kaufmann Publishers Inc., 2008.
[35] K. Finstad, “The System Usability Scale and Non-Native English Speakers,” English, vol. 1, no. 4, pp. 185–188, 2006.
[36] A. Bangor, “Determining What Individual SUS Scores Mean: Adding an Adjective Rating Scale,” J. usability Stud., vol. 4, no. 3, pp. 114–123, 2009.
[37] “Gephi,” Wikipedia. [Online]. Available: https://en.wikipedia.org/wiki/Gephi. [Accessed: 14-Oct-2015].		zh_TW