越來越多企業想更精確掌握使用者的需求，以提昇網路行銷績效，本研究對網路使用者進行兩個研究分析：第一個研究為Facebook粉絲專頁使用者的回應行為，將42,953筆Facebook 粉絲頁使用者以兩階段集群分析法進行偏好分群，定義出7個關鍵特徵族群，包括：低調潛水型粉絲、資訊收集型粉絲、健康時尚型粉絲、視覺淑女型粉絲、知識閱讀型粉絲、消費購物型粉絲、高調活躍型粉絲，而且發現超過66%的Facebook粉絲頁使用者不願按讚或留言。
第二個研究則運用網路爬文、及語意分析方法（TF-IDF、SVM），蒐集2016年iPhone 7上市前、後共1,886則標題、6,326則貼文，分析網路使用者在四種網路平台的發文與回文行為、並對正負面向的討論進行歸類，彙整出網路使用者發文行為特徵。結果發現近70%的網路使用者不會有主動發文與回文行為，另一方面，企業若透過討論區來與網路使用者溝通，會有較高的互動。
綜合兩個研究，本研究發現多數網路使用者偏向低調潛水、不輕易表態、負面口碑未必會有迅速擴散等結果，希望可以作為企業在制定網路社群行銷、與網路口碑回應策略時的參考。

More and more enterprises are trying to understand the implications of online users' behavior in the Internet world so as to better realize the customers demand. To discovery the preferences and needs of users in order to achieve more effective online marketing performance, this study conducted two behavioral research analyzes of Internet users. The first research collected the responses of Facebook fan pages users, Grouping 42,953 Facebook fan page users in two-step clustering for grouping of preferences, trying to define the preference attributes of online users for Facebook fan pages, so that when the enterprise carries out online social marketing can be more precise segmentation. The results of first research show 7 clusters, each with their differences and key characteristics, namely the Lurk Fans, Informational Fans, Health and Beauty Fans, Visual Ladies Fans, Intellectual Reader Fans, Consumption and Shopping Fans, and the Highly Active Fans. Among them, the Lurk Fans as high as 36%, and total of more than 66% of Facebook fans page users lacked to presented their intentions.
The second research collected total 1,886 headlines and 6,326 posts related iphone 7 commercial launch at Sep. Y2016, using online crawling and semantic analysis methods （TF-IDF and SVM）to analyze the online users send text and palindromes on the four difference internet platforms, research and classify the positive and negative word-of-mouth discussions for online users, and sort out the online user behavior characteristics for enterprises practice reference. Nearly 70% of Internet users do not have the initiative to send and receive essay behavior, and most of the online users will not post or response, enterprises expected have a higher interaction with online users, could be through discuss at the forum.
Based on the results of the both research, we hope this study can serve as a reference for enterprises operating Facebook fan pages and Internet word of mouth, and can be used as a principle guideline for business operators in formulating social network marketing strategies.

1. Abbasi, A.Z., Islam, N. & Shaikh, Z.A. (2014). A review of wireless sensors and networks' applications in agriculture. Computer Standards & Interfaces, 36 (2), 263-270.
2. Ajzen, I., & Fishbein, M. (1975). A Bayesian analysis of attribution processes. Psychological Bulletin, 82(2), 261-277.
3. Ali, K., & Van Stam, W. (2004). TiVo: making show recommendations using a distributed collaborative filtering architecture. Paper presented at the Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining.
4. Amos, C., Holmes, G., & Strutton, D. (2008). Exploring the relationship between celebrity endorser effects and advertising effectiveness: A quantitative synthesis of effect size. International Journal of Advertising, 27, 209–234.
5. Anderson, C.A., & Bushman, B.J. (2002). The Effects of Media Violence on Society. Science, 295(5564), 2377-2379.
6. Anderson, E.W., & Sullivan, M.W., (1993). The Antecedents and Consequences of Customer Satisfaction for Firms. Marketing Science, 12, 2, 125-143.
7. Araujo, T., & Neijens, P. (2012). Friend me: which factors influence top global brands participation in social network sites. Internet Research, 22(5), 626-640.
8. Araujo, T., Neijens, P., & Vliegenthart, R. (2017). Getting the word out on Twitter: the role of influentials, information brokers and strong ties in building word-of-mouth for brands. International Journal of Advertising, 36(3), 496-513.
9. Armstrong, A., & Hagel, J. (2000). The real value of online communities. Knowledge and Communities, 85-95. http://dx.doi.org/10.1016/B978-0-7506-7293-1.50009-3
10. Baghdadi, Y. (2013). From e-commerce to social commerce: A framework to guide enabling cloud computing. Journal of Theoretical and Applied Electronic Commerce Research, 8(3), 12-38. http://dx.doi.org/10.4067/S0718-18762013000300003
11. Benevenuto, F., Rodrigues, T., Cha, M., & Almeida, V. (2012). Characterizing user navigation and interactions in online social networks. Information Sciences, 195, 1-24.
12. Bennett, J., & Lanning, S. (2007). The Netflix prize. Paper presented at the Proceedings of KDD cup and workshop.
13. Boyd, D. M. & Ellison, N. B. (2007). Social Network Sites: Definition, History and Scholarship, Journal of Computer-Mediated Communication, 13(1), 210-230.
14. Brooks, R. C. (1957). Word-of-Mouth" Advertising in Selling New Products. The Journal of Marketing, 22(2), 154-161
15. Brophy, L. M., Reece, J. E., & McDermott, F. (2006). A cluster analysis of people on Community Treatment Orders in Victoria, Australia. International journal of law and psychiatry, 29(6), 469-481.
16. Brugmann, J. &; Prahalad, C. K. (2007). Cocreating business's new social compact. Harvard Business Review, 85(2), 80.
17. Bush, A. J., Martin, C. A., and Bush, V. D. (2004). Sports celebrity influence on the behavioral intentions of generation Y. Journal of Advertising Research, 44, pp. 108–119.
18. CheckFacebook. (2013). Facebook Marketing Statistics, Demographics. Reports and News. Retrieved from http://www.checkfacebook.com
19. Chen, M.-S., Han, J., & Yu, P. S. (1996). Data mining: an overview from a database perspective. Knowledge and data Engineering, IEEE Transactions on, 8(6), 866-883.
20. Chevalier, J. A., & Mayzlin, D. (2006). The effect of word of mouth on sales: Online book reviews. Journal of Marketing Research, 43(3), 345-354.
21. Cho, D., & Kwon, K.H. (2015). The impacts of identity verification and disclosure of social cues on flaming in online user comments. Computers in Human Behavior, 51, 363-372.
22. Damasio, A. R. (1994). Descartes' error: Emotion, reason, and the human brain. New York: Avon Books.
23. De Vries, F.T., Hoffland, E., van Eekeren, N., Brussaard, L. & Bloem, J. (2006). Fungal/bacterial ratios in grasslands with contrasting nitrogen management. Soil Biol. Biochem., 38, 2092- 2103.
24. Dehghani, M., & Tumer, M. (2015). A research on effectiveness of Facebook advertising on enhancing purchase intention of consumer. Computers in Human Behavior, 49, 597-600. http://dx.doi.org/10.1016/j.chb.2015.03.051.
25. Derbaix, C., & Vanhamme, J. (2003). Inducing word-of-mouth by eliciting surprise–a pilot investigation. Journal of economic psychology, 24(1), 99-116.
26. Deshpande, R. (1982). Paradigms lost’: On theory and method in research in marketing. Journal of Marketing, 47 (4), 101–110.
27. Dholakia, U. M., & Durham, E. (2010). One café chain’s Facebook experiment. Harvard Business Review, 88(3), 26. Retrieved from https://hbr.org/2010/03/one-cafe-chains-facebook-experiment
28. Edelmann, Noella (2013). Reviewing the Definitions of “Lurkers” and Some Implications for Online Research. Cyberpsychology, Behavior, and Social Networking, 16(9), 645-649.
29. Eisenhardt, K. (1989). Agency theory: An assessment and Review. Academy of Management Review, 14(1), 57-74.
30. Eldon, E. (2011). Facebook’s Start-and-Stop Growth in China, Taiwan and Hong Kong – A Closer Look. InsideFacebook. Retrieved from http://www.insidefacebook.com/2011/03/02/odd- Facebook-traffic-growth-patterns-emerge-in-greater-china/
31. Erdogan, B.Z. (1999). Celebrity Endorsement: A Literature Review. Journal of Marketing Management, 15(4), 291-314.
32. Fagerstrem, A., & Ghinea, G. (2010). Web 2.0’s marketing impact on low-involvement consumers. Journal of Interactive Advertising, 10(2), 67-71. http://dx.doi.org/10.1080/15252019.2010.10722171
33. Flatley, M.E. (2005). Blogging for enhanced teaching and learning. Business Communication Quarterly, 68(1), 77-80。
34. Flavián, C., & Guinalíu, M. (2006). Consumer trust, perceived security and privacy policy: Three basic elements of loyalty to a web site. Industrial Management & Data System, 106(5), 601-620.http://dx.doi.org/10.1108/02635570610666403.
35. Fortin, D. R., & Dholakia, R. R. (2005). Interactivity and Vividness Effects on Social Presence and Involvement with a Web-Based Advertisement. Journal of Business Research, 58, 387- 396.
36. Gilley, J.W., Morris, M.L., Waite, A. M., Coates, T., & Veliquette, A. (2010). Integrated Theoretical Model for Building Effective Teams. Advances in Developing Human Resources, 12( 1), 7-28.
37. Gupta, P., & Harris, J. (2010). How e-WOM recommendations influence product consideration and quality of choice: A motivation to process information perspective. Journal of Business Research, 63(9-10), 1041-1049. http://dx.doi.org/10.1016/j.jbusres.2009.01.015
38. Gupta, P., & Harris, J. (2010). How e-WOM recommendations influence product consideration and quality of choice: A motivation to process information perspective. Journal of Business Research, 63(9), 1041-1049.
39. Hajli, N. (2015). Social commerce constructs and consumer’s intention to buy. International Journal of Information Management, 35(2), 183-191. http://dx.doi.org/10.1016/j.ijinfomgt.2014.12.005
40. Hanani, U., Shapira, B., & Shoval, P. (2001). Information filtering: Overview of issues, research and systems. User Modeling and User-Adapted Interaction, 11(3), 203-259.
41. Hennig-Thurau, T., Hofacker, C. F., & Bloching, B. (2013). Marketing the pinball way: Understanding how social media change the generation of value for consumers and companies. Journal of Interactive Marketing, 27(4), 237-241. http://dx.doi.org/10.1016/j.intmar.2013.09.005
42. Hollenbeck, C. R., & Kaikati, A. M. (2012) Consumers’ use of brands to reflect their actual and ideal selves on Facebook. International Journal of Research in http://dx.doi.org/10.1016/j.ijresmar.2012.06.002
43. Holzner, S. (2008). Facebook marketing: leverage social media to grow your business. Pearson Education.
44. Huang, Z., & Benyoucef, M. (2013). From e-commerce to social commerce: A close look at design features. Electronic Commerce Research and Applications, 12(4), 246-259. http://dx.doi.org/10.1016/j.elerap.2012.12.003
45. Jahn, B., & Kunz, W. (2012). How to transform consumers into fans of your brand. Journal of Service Management, 23(3), 344-361. http://dx.doi.org/10.1108/09564231211248444
46. Jansen, B.J., Zhang, M., Sobel, K., & Chowdury, A. (2009). Twitter power: Tweets as electronic word of mouth. Journal of the American Society for Information Science and Technology, 60, 2169-2188.
47. Jung, J. J. (2011). Service chain-based business alliance formation in service-oriented architecture. Expert Systems with Applications, 38(3), 2206-2211.
48. Kähr, A., Nyffenegger, B., Krohmer, H,, & Hoyer , W.D. (2016). When Hostile Consumers Wreak Havoc on Your Brand: The Phenomenon of Consumer Brand Sabotage. Journal of Marketing, 80(3), 25-41.
49. Kang, J., Tang, L., & Fiore, A. M. (2014). Enhancing consumer–brand relationships on restaurant Facebook fan pages: Maximizing consumer benefits and increasing active participation. International Journal of Hospitality Management, 36, 145-155.
50. Katz, R., Tushman, M., & Allen, T.J. (1995). The influence of supervisory promotion and network location on subordinate careers in a dual ladder RD&E setting. Management Science, 41(5), 848−863.
51. Keller, K.L. (1993). Conceptualizing, Measuring, and Managing Customer-Based Brand Equity. Journal of Marketing, 57(1), 1-22.
52. Kemp, S. (2015). Digital, Social & Mobile Worldwide in 2015. Retrieved from http://wearesocial.net/blog/2015/01/digital-social-mobile-worldwide-2015/ .
53. Khan, I., Weishaar, B., Karasyov, V., Wei, D., Bazhenova, E., & Hong, A. (2010). Nothing but net. JP Morgan.
54. Khan, K., Baharudin, B., Khan, A., & Ullah, A. (2014). Mining opinion components from unstructured reviews: A review. Journal of King Saud University - Computer and Information Sciences, 26, 258-275
55. Kim, J. (2013). Relationship between Facebook usage and self-efficacy among collegiate athletes. Media Watch, 4(3), 364-374. Retrieved from http://www.i- scholar.in/index.php/mw/article/view/53694
56. Li, Y. M., Hsiao, H. W., & Lee, Y. L. (2013). Recommending social network applications via social filtering mechanisms. Information Sciences, 239(1), 18-30. http://dx.doi.org/10.1016/j.ins.2013.03.041
57. Li, Y.M., Lai, C.Y., & Chen, C.W. (2011). Discovering influencers for marketing in the blogosphere. Information Sciences, 181(23), 5143-5157.
58. Liang, D., Tsai, C.F., & Wu, H.T. (2015). The effect of feature selection on financial distress prediction. Knowledge-Based Systems, 73, 289–297.
59. Lin, K.-Y., & Lu, H.-P. (2011a). Intention to continue using Facebook fan pages from the perspective of social capital theory. CyberPsychology, Behavior, and Social Networking, 14(10), 565-570.
60. Lin, K.-Y., & Lu, H.-P. (2011b). Why people use social networking sites: An empirical study integrating network externalities and motivation theory. Computers in Human Behavior, 27(3), 1152-1161.
61. Linden, G., Smith, B., & York, J. (2003). Amazon.com recommendations: Item-to-item collaborative filtering. Internet Computing, IEEE, 7(1), 76-80.

62. Lipsman, A., Mudd, G., Rich, M., & Bruich, S. (2012). The Power of" Like": How Brands Reach (and Influence) Fans through Social-Media Marketing. Journal of Advertising research, 52(1), 40.

63. Liu, H., Hu, Z., Mian, A., Tian, H., & Zhu, X. (2014). A new user similarity model to improve the accuracy of collaborative filtering. Knowledge-Based Systems, 56, 156-166. http://dx.doi.org/10.1016/j.knosys.2013.11.006.
64. Lynch, J.G., & Ariely, D. (2000), Search Costs Affect Competition on Price, Quality, and Distribution.·Journal of Marketing Science, 19(1), 83-103.
65. Mano, H., & Oliver, R. L. (1993). Assessing the dimensionality and structure of the consumption experience: evaluation, feeling, and satisfaction. Journal of Consumer Research, 20(3), 451-466.
66. McCracken, G. (1988). The long interview. Newbury Park, CA: Sage.
67. Menon, A. l, Bharadwaj, S. G., Adidam, P. T., & Edison, S. W. (1999). Antecedents and consequences of marketing strategy making: A model and a test. Journal of Marketing, 63 (2), 18–40.
68. Moses, L. (2013). Data Points: Brand Fans People have more brands as friends than ever on Facebook. Adweek. Retrieved from http://www.adweek.com/news/advertising-branding.
69. Mudambi, S. M., & Schuff, D. (2010). What makes a helpful online review? A study of customer reviews on Amazon. com. MIS Quarterly, 34(1), 185-200.
70. Ng, C. S. P. (2013). Intention to purchase on social commerce websites across cultures: A cross-regional study. Information & Management, 50(8), 609-620. http://dx.doi.org/10.1016/j.im.2013.08.002
71. Paquette, D. E. (2009). Use of technology in the orthodontic practice: A day in the life. American Journal of Orthodontics and Dentofacial Orthopedics, 136(4), 607-610.

72. Pongsakornrungsilp, S., & Schroeder, J. E. (2011). Understanding value co-creation in a co-consuming brand community. Marketing Theory, 11(3), 303-324. http://dx.doi.org/10.1177/1470593111408178
73. Pöyry, E., Parvinen, P., & Malmivaara, T. (2013). Can we get from liking to buying? Behavioral differences in hedonic and utilitarian Facebook usage. Electronic Commerce Research and Applications, 12(4), 224-235. http://dx.doi.org/10.1016/j.elerap.2013.01.003.
74. Pujol, J. M., Erramilli, V., Siganos, G., Yang, X., Laoutaris, N., Chhabra, P., & Rodriguez, P. (2011). The little engine (s) that could: scaling online social networks. ACM SIGCOMM Computer Communication Review, 41(4), 375-386.

75. Ramaswamy, S., & Rose, K. (2011). Adaptive cluster distance bounding for high- dimensional indexing. IEEE Transactions on Knowledge and Data Engineering, 23(6), 815-830. http://dx.doi.org/10.1109/TKDE.2010.59
76. Rheingold, H. (2000). The virtual community: Homesteading on the electronic frontier. Cambridge, MA: The MIT Press.
77. Richins, M.L. (1997). Measuring emotions in the consumption experience. Journal of Consumer Research, 24(2), 127-146.
78. Richter, D., Riemer, P. D. K., & vom Brocke, J. (2011). Internet social networking. Wirtschaftsinformatik, 53(2), 89-103.

79. Rime, B., Mesquita, B., Boca, S., &; Philippot, P. (1991). Beyond the emotional event: Six studies on the social sharing of emotion. Cognition &; Emotion, 5(5-6), 435-465.
80. Rogers, E.M. (2003). Diffusion of innovations. New York: Free Press.
81. Romm, C., Pliskin, N., & Clarke, R. (1997). Virtual communities and society: Toward and Well man, Barry and Haythornthwait, Caroline, The Internet in Everyday Life.
82. Sarwar, A., Haque, A., & Yasmin, F. (2013). The usage of social network as a marketing tool: Malaysian Muslim consumers’ perspective. International Journal of Academic Research in Economics and Management Sciences, 2(1), 93-102. Retrieved from http://www.hrmars.com/admin/pics/1491.pdf
83. Satish, S. M., & Bharadhwaj, S. (2010). Information search behaviour among new car buyers: A two-step cluster analysis. IIMB Management Review, 22(1-2), 5-15.
84. Schiopu, D. (2010) Applying two-step cluster analysis for identifying bank customers’ profile.
85. Shafer, D. (1999). Dan Shafer’s proposed 10 rules for measuring online communities. A Retrieved October 15, 2010,from http://www.onlinecommunityreport.com/features/metrics.
86. Shao, G. (2009). Understanding the appeal of user-generated media: a uses and gratification perspective. Internet Research, 19(1), 7-25.
87. Sharma, S., & Crossler, R. E. (2014). Disclosing too much? Situational factors affecting information disclosure in social commerce environment. Electronic Commerce Research and Applications, 13(5), 305-319. http://dx.doi.org/10.1016/j.elerap.2014.06.007
88. Stephen, A. T., & Toubia, O. (2010). Deriving value from social commerce networks. Journal of Marketing Research, 47(2), 215-228. http://dx.doi.org/10.1509/jmkr.47.2.215
89. Strand, J. L. (2011). Facebook: Trademarks, fan pages, and community pages. Intellectual Property and Technology Law Journal, 23(1), 10-13. Retrieved from http://search.proquest.com/docview/838987732?pq-origsite=gscholar.
90. Swallow (2011). Top reasons why consumers unsubscribe via e-mail, Facebook & Twitter. Retrieved from http://mashable.com/2011/02/08/why-consumers- unsubscribe/
91. Tanimoto, J., & Fujii, H. (2003). A study on diffusional characteristics of information on a human network analyzed by a Multi-Agent simulator. The Social Science Journal, 40(3), 479-485.
92. Teng E.1., Lu, P.H., & Cummings, J.L. (2007). Deficits in facial emotion processing in mild cognitive impairment. Dement Geriatr Cogn Disord, 23(4), 271-279.
93. Ullah, R., Zeb, A. & Kim, W. (2015). The impact of emotions on the helpfulness of movie reviews. Journal of Applied Research and Technology, 13(3), 359-363.
94. Voorhees, E. M. (1986). Implementing agglomerative hierarchic clustering algorithms for use in document retrieval. Information Processing & Management, 22(6), 465-476.
95. Waldman, D.A., Berson, Y., & Keller, R.T. (2009). Leadership and organizational learning. The Leadership Quarterly, 20, 34-48.
96. Warner, W. L. (1941). Social Anthropology and the Modern Community. American Journal of Sociology, 46(6), 785-796.
97. Wellman, B., & Gulia, M. (1999). Net-surfers don’t ride alone: Virtual communities as communities. In B. Wellman (Eds.), Networks in the global village: Life in contemporary communities (pp. 331-366). Boulder, CO: Westview Press.
98. Williamson, D. A. (2011). Worldwide Social Network Ad Spending: ARising Tide. eMarketer.com. Retrieved from http://www.emarketer.com/Report.aspx?code=emarketer_2000692
99. Wilson, K., Fornasier, S., & White, K. M. (2010). Psychological predictors of young adults’ use of social networking sites. Cyberpsychology, Behavior, and Social Networking, 13(2), 173-177. http://dx.doi.org/10.1089/cyber.2009.0094
100. Yang, X., Guo, Y., & Liu, Y. (2013). Bayesian-Inference-Based Recommendation in Online Social Networks. Parallel and Distributed Systems, IEEE Transactions on, 24(4), 642-651.
101. Ye, Q., Shi, W., & Li, Y. (2006). Sentiment Classification for Movie Reviews in Chinese by Improved Semantic Oriented Approach. System Sciences, Proceedings of the 39th Annual Hawaii International Conference on System Sciences (HICSS'06).
102. Zaltman, G. (1997). Rethinking Market Research: Putting People Back In. Journal of Marketing Research, 34 (4), 424–437.
103. Zhao, S., Grasmuck, S., & Martin, J. (2008). Identity construction on Facebook: Digital empowerment in anchored relationships. Computers in Human Behavior, 24(5), 1816-1836. http://dx.doi.org/10.1016/j.chb.2008.02.012.
104. Zhao, Y., & Karypis, G. (2004). Empirical and theoretical comparisons of selected criterion functions for document clustering. Machine Learning, 55(3), 311-331.