以社群平台研究區域與議題之間的關係

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：56

、訪客IP：18.116.88.168

姓名

江子良(Zih-Liang Chiang) 查詢紙本館藏

畢業系所

企業管理學系

論文名稱

以社群平台研究區域與議題之間的關係
(Investigating the relationships between geographical closeness and popular topic similarity on Twitter)

相關論文

★ 在社群網站上作互動推薦及研究使用者行為對其效果之影響	★ 以AHP法探討伺服器品牌大廠的供應商遴選指標的權重決定分析
★ 以AHP法探討智慧型手機產業營運中心區位選擇考量關鍵因素之研究	★ 太陽能光電產業經營績效評估－應用資料包絡分析法
★ 建構國家太陽能電池產業競爭力比較模式之研究	★ 以序列採礦方法探討景氣指標與進出口值的關聯
★ ERP專案成員組合對績效影響之研究	★ 推薦期刊文章至適合學科類別之研究
★ 品牌故事分析與比較-以古早味美食產業為例	★ 以方法目的鏈比較Starbucks與Cama吸引消費者購買因素
★ 探討創意店家創業價值之研究- 以赤峰街、民生社區為例	★ 以領先指標預測企業長短期借款變化之研究
★ 應用層級分析法遴選電競筆記型電腦鍵盤供應商之關鍵因子探討	★ 以互惠及利他行為探討信任關係對知識分享之影響
★ 結合人格特質與海報主色以類神經網路推薦電影之研究	★ 資料視覺化圖表與議題之關聯

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

[檢視]

[下載]

本電子論文使用權限為同意立即開放。
已達開放權限電子全文僅授權使用者為學術研究之目的，進行個人非營利性質之檢索、閱讀、列印。
請遵守中華民國著作權法之相關規定，切勿任意重製、散佈、改作、轉貼、播送，以免觸法。

摘要(中)

由於社群網路的蓬勃發展，人們越來越依賴社群網路關注時事動態，並成為人與人之間討論的議題，在社群網路的特性中，發文狀態擁有簡短、在地的特性，而累積的資料數量卻十分龐大，卻不見學者研究如何利用這些發文特性，研究議題彼此間的區域影響關係，並加以影響日常生活人們討論的關心議題。
本研究收錄美國熱門社群網路平台 Twitter 使用者張貼的訊息、區域資料，以16州使用者為例，選取適合的使用者討論的議題數量，並分群議題區域，探討鄰近城市是否會關心相似的議題，找出重要的議題區域以及各城市在議題之間的關聯。
本研究的實驗結果發現，本研究的方法能證實鄰近城市關心類似的議題，並加以找出適合的議題。這些實驗結果，應有助於地方性實體行銷活動、地方性網路廣告投放，以及政府地方性政策或服務行銷商，研判如何在有限的財務、人力、時間資源內，實現策略管理目標。

摘要(英)

Since the rise of social media, people spend more time reading news articles from different social media platforms, making it a relevant and important for people to discuss in person. One characteristic of social networking platforms is their location-based features where one can tag their current whereabouts. Many people have been doing this in the past however, researchers have not delved into the relationship between the occurrence from one region to another; this implies that a topic occurring in some other place can affect the topic of discussion of another.

The research collects posts with location information from the users of the most popular social media platforms, such as Twitter, which enables them to find proper topics in those regions. The research results are dedicated to help local marketing strategies, local advertising postings, local service strategies, and local government strategies; mainly, projects with limited financial budget, human resource, and time to achieve specific goals.

The results of this research will help the efficiency of our current resource that will ultimately reach our goals.

關鍵字(中)

★ 社群網站
★ 社群網路
★ 議題分析
★ 議題
★ 地區
★ Twitter

關鍵字(英)

★ social network
★ text mining
★ topic mining
★ region
★ twitter
★ social website

論文目次

中文摘要 i
Abstract ii
目錄 iv
表目錄 vi
圖目錄 vii
第一章緒論 1
1.1研究背景與動機 1
1.2 研究目的 3
1.3研究架構 3
第二章文獻探討 5
2.1 Twitter社群網站 5
2.2 議題分析 6
2.3 自組織映射圖網路 8
第三章研究方法 11
3.1 研究設計 11
3.1.1資料蒐集 12
3.1.2資料整理 13
3.1.3議題與區域之間的關聯 15
3.1.4適當的議題與區域數量 15
第四章研究實作 20
4.1 資料分析 20
4.1.1 資料前處理 20
4.1.2 實驗結果 20
第五章結論與未來研究建議 35
5.1研究結論 34
參考文獻 35

參考文獻

［1］ . Wu, S., Hofman, J. M., Mason, W. A., & Watts, D. J. (2011). Who says what to whom on Twitter. In Proceedings of the 20th International Conference on World Wide Web (pp. 705–714). New York, NY: ACM. Retrieved from http://doi. org/10.1145/1963405.1963504
［2］ Zhang, Y., & Leung, L. (2014). A review of social networking service (SNS) research in communication journals from 2006 to 2011. New Media & Society. Retrieved from http://doi. org/10.1177/1461444813520477
［3］ Zhou, Z., Bandari, R., Kong, J., Qian, H., & Roychowdhury, V. (2010). Information resonance on Twitter: Watching Iran. In Proceedings of the First Workshop on Social Media Analytics (pp. 123–131). New York, NY: ACM. Retrieved from http:// doi.org/10.1145/1964858.1964875
［4］ Yang, S. C., Chang, W. T., Hsiao, Y. T., & Chen, B. Y. (2014). The effects of perceived value on Facebook post sharing inten- tion. In Proceedings of the 12th International Conference on Advances in Mobile Computing and Multimedia (pp. 444–450). New York, NY: ACM. Retrieved from http://doi. org/10.1145/2684103.2684171
［5］ Nielsen, R. K., & Schrøder, K. C. (2014). The relative importance of social media for accessing, finding, and engaging with news: An eight-country cross-media comparison. Digital Journalism, 2, 472–489. Retrieved from http://doi.org/10.1080/21670811. 2013.872420
［6］ Nunnally J C. Psychometric theory. New York: McGraw Hill, 1967, 640 p. Retrieved from http://garfield.library.upenn.edu/classics1979/A1979HZ31300001.pdf
［7］ Susarla, A., Oh, J.-H., & Tan, Y. (2012). Social networks and the diffusion of user-generated content: Evidence from YouTube. Information Systems Research, 2323–41. Retrieved from http:// doi.org/10.1287/isre.1100.0339
［8］ Rosengard, D., Tucker-McLaughlin, M., & Brown, T. (2014). Students and social news. How college students share news through social media. Electronic News, 8, 120–137. Retrieved from http://doi.org/10.1177/1931243114546448
［9］ Morgan, J. S., Lampe, C., & Shafiq, M. Z. (2013). Is news sharing on Twitter ideologically biased? In Proceedings of the 2013 Conference on Computer Supported Cooperative Work (pp. 887–896). New York, NY: ACM. Retrieved from http://doi. org/10.1145/2441776.2441877
［10］ Myers, S. A., Zhu, C., & Leskovec, J. (2012). Information diffusion and external influence in networks (CoRR, abs/1206.1331). Retrieved from http://arxiv.org/abs/1206.1331
［11］ TrackMaven(2016). The Content Marketing Paradox.
［12］ Weber & Monge(2011). The Flow of Digital News in a Network of Sources, Authorities, and Hubs
［13］ S Arora (2012). Learning Topic Models-Going beyond SVD
［14］ KS Gurumoorthy (2013).Beyond SVD: Sparse Projections Onto Exemplar Orthonormal Bases for Compact Image Representation
［15］ Thomas Hofmann(1999).Probabilistic Latent Semantic Analysis
［16］莊克仁(2015). 圖解民意與公共關係(p229)
［17］ Shuang Yang, Chunfeng Yuan, Weiming Hu(2014).A Hierarchical Model Based on Latent Dirichlet Allocation for Action Recognition
［18］ HONGZHI YIN(2011), Modeling Location-Based User Rating Profiles for Personalized Recommendation
［19］ Sueli A. Mingoti *, Joab O. Lima(2005).Comparing SOM neural network with Fuzzy c-means, K-means and traditional hierarchical clustering algorithms
［20］ Yi Yang, Doug Downey(2015).Efficient Methods for Incorporating Knowledge into Topic Models
［21］ A Asuncion(2009).On Smoothing and Inference for Topic Models
［22］ Weeks, B. E., & Holbert, R. L. (2013).Predicting dissemination of news content in social media: A focus on reception, friend- ing, and partisanship. Journalism & Mass Communication Quarterly, 90, 212–232. Retrieved from http://doi.org/10.1177/ 1077699013482906
［23］ Anna Sophie Kümpel, Veronika Karnowski, and Till Keyling(2015).News Sharing in Social Media: A Review of Current Research on News Sharing Users, Content, and Networks
［24］ H. Kwak (2010). What is Twitter, a social network or a news media? ACM. Retrived from dl.acm.org/citation.cfm?id=1772751
［25］ X Cheng (2014). BTM: Topic Modeling over Short Texts. IEEE Retrived from https://www.computer.org/csdl/trans/tk/preprint/06778764.pdf
［26］ Ma, L., Lee, C. S., & Goh, D. H.-L. (2013). Investigating influential factors influencing users to share news in social media: A diffusion of innovations perspective. In Proceedings of the 13th ACM/IEEE-CS Joint Conference on Digital Libraries (pp. 403–404). New York, NY: ACM. Retrieved from http://doi. org/10.1145/2467696.2467749
［27］ Ma, L., Lee, C. S., & Goh, D. H.-L. (2014). Understanding news sharing in social media. An explanation from the diffusion of innovations theory. Online Information Review, 38, 598–615. Retrieved from http://doi.org/10.1108/OIR-10-2013-0239
［28］ M Cataldi, (2010).Retrieved from http://users.cis.fiu.edu/~lzhen001/activities/KDD_USB_key_2010/workshops/W07%20MDMKDD2010/MDMKDD2010_04.pdf
［29］ Mitchell, A., Holcomb, J., & Page, D. (2013). News use across social media platforms. Pew Research Center. Retrieved from http://www.journalism.org/files/2013/11/News-Use-Across- Social-Media-Platforms1.pdf
［30］ Holton, A. E., Baek, K., Coddington, M., & Yaschur, C. (2014). Seeking and sharing: Motivations for linking on Twitter. Communication Research Reports, 31, 33–40. Retrieved from http://doi.org/10.1080/08824096.2013.843165
［31］ Hong, L., Dan, O., & Davison, B. D. (2011). Predicting popular messages in Twitter. In Proceedings of the 20th International Conference Companion on World Wide Web (pp. 57–58). New York, NY: ACM. Retrieved from http://doi. org/10.1145/1963192.1963222
［32］ Horan, T. J. (2013). “Soft” versus “hard” news on microblogging networks. Information, Communication & Society, 16, 43–60. Retrieved from http://doi.org/10.1080/1369118X.2011.649774
［33］ Hu, M., Liu, S., Wei, F., Wu, Y., Stasko, J., & Ma, K.-L. (2012). Breaking news on Twitter. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (pp. 2751–2754). New York, NY: ACM. Retrieved from http://doi. org/10.1145/2207676.2208672
［34］ Kim, M., Newth, D., & Christen, P. (2014). Trends of news dif- fusion in social media based on crowd phenomena. In Proceedings of the Companion Publication of the 23rd International Conference on World Wide Web Companion (pp. 753–758). Geneva, Switzerland: International World Wide Web Conferences Steering Committee. Retrieved from http://doi.org/10.1145/2567948.2579325
［35］ M Cataldi (2010) Retrived from http://users.cis.fiu.edu/~lzhen001/activities/KDD_USB_key_2010/workshops/W07%20MDMKDD2010/MDMKDD2010_04.pdf
［36］ Ju, Jeong, & Chyi, 2014 Will Social Media Save Newspapers? Retrieved from http://www.tandfonline.com/doi/abs/10.1080/17512786.2013.794022#.V0Po8ZN950s
［37］ R Zafarani 2014 Social Media Mining: An Introduction Retrieved from http://dmml.asu.edu/smm/SMM.pdf
［38］ Kiousis, S. (2002). Interactivity: A concept explication. New Media & Society, 4, 355–383. Retrieved from http://doi. org/10.1177/146144480200400303
［39］ Hao Wang and Wu-Jun (2015) IEEE. Retrived from http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6940223
［40］ Kostkova, P., Szomszor, M., & St.Louis, C. (2014). #swineflu: The use of Twitter as an early warning and risk communica- tion tool in the 2009 swine flu pandemic. ACM Transactions on Management Information Systems (TMIS), 5(2), 8:1–8:25. Retrieved from http://doi.org/10.1145/2597892
［41］ Fahmy, N. A.-S. (2012). News diffusion and facilitation of conver- sation. Journal of Middle East Media, 8(1), 1–21.
［42］ Ferrara, E., Interdonato, R., & Tagarelli, A. (2014). Online popularity and topical interests through the lens of Instagram. In Proceedings of the 25th ACM Conference on Hypertext and Social Media (pp. 24–34). New York, NY: ACM. Retrieved from http://doi.org/10.1145/2631775.2631808
［43］ Forest, A. L., & Wood, J. V. (2012). When social networking is not working: Individuals with low self-esteem recognize but do not reap the benefits of self-disclosure on Facebook. Psychological Science, 23, 295–302. Retrieved from http://doi. org/10.1177/0956797611429709
［44］ Fuchs, C. (2014). Social media: A critical introduction. Thousand Oaks, CA: SAGE.
［45］ Gil de Zúñiga, H., Jung, N., & Valenzuela, S. (2012). Social media use for news and individuals’ social capital, civic engagement and political participation. Journal of Computer-Mediated Communication, 17, 319–336. Retrieved from http://doi. org/10.1111/j.1083-6101.2012.01574.x
［46］ Boczkowski, P. J., & Mitchelstein, E. (2012). How users take advan-
tage of different forms of interactivity on online news sites: Clicking, e-mailing, and commenting. Human Communication Research, 38, 1–22. Retrieved from http://doi.org/10.1111/ j.1468-2958.2011.01418.x
［47］ boyd, d. (2015). Social media: A phenomenon to be analyzed. Social Media + Society, 1. Retrieved from http://doi. org/10.1177/2056305115580148

指導教授

許秉瑜(Ping-Yu Hsu)

審核日期

2018-1-10

推文