CHAPTER 6 CONCLUSION
6.2 Limitations and Future Studies
There are several limitations and future studies to this research. First, due to the privacy issue, it is difficult to extract online personal data (e.g. social information and purchase histories etc.). Therefore, we invite participants to join in the experiments. If there are more users recruited and engaged, the accuracy of the proposed mechanisms will be more improved. Besides, in the current paper, the online postings in social media are used as social interactions for analyzing the strength of interpersonal relationships. In social media, there are many ways (e.g., messaging, applications, photo uploads, chat etc.) for users to interact with others. The analysis of relationship strength would be more comprehensive if more other interaction ways are considered.
Second, the essential concept of this research is that the closer friends might understand our preferences, habits, and needs better, so their opinions should be more reliable and suitable than others. Currently, the appraisal for purchase decision, key person for information deliver, and the reference values for seller selection are mainly estimated only by considering the evaluations given by close friends. However, there likely exist many good feedbacks contributed by people who are strange to us. How to further consider these trusty and referential evaluations and balance the impacts of opinions extracted from public and from friend should be a desirable extension direction.
-99-
Third, the mechanisms regard to analyze nature langue might not work with high effectiveness. As our observations, users used to express their opinions by short sentences in the social media. As a result, the information extracted from the online postings might not be sufficient to represent, for example, the criteria and evaluations of a product. Due to the problem of ambiguous nature langue (e.g. the user might tend to improvise new words and abbreviation) and they are a matter of taste, the semantic analysis might not well extract and represent the criteria and evaluations of a product.
Besides, although the current adjective graph could satisfactorily identify most of the adjectives with high usage frequency, the adjective orientation might not be easily identifiable if users use words with low usage frequency. So that, the approach to extracting needed information from the online postings expressed in natural language could be elaborated.
Fourth, the directions of trustworthiness or social influence between users should be taken into considerations. It is one of important to the social advertising path planning issue. While determining the possible transition states and the transition probabilities, the concept of trust or the tie strength analysis between social nodes should be taken into account. The ratio based determination has possibility of data bias regarding the frequency of use under the period of data collection. Besides, in electronic marketplace, not only buyers could evaluate the reputations of sellers but also sellers could evaluate reputations of buyers. It would be also interesting to evaluate the trustworthiness of referral candidates from the perspective of sellers in the marketplace.
Fifth, the different social factors could be taken into consideration while building the social based mechanisms. The different social factor could be taken into consideration while formulating the diffusion reward function in the social advertising path planning mechanism. For example, if a social node located as a structural hole, the marketer might gain relatively great diffusion reward from him/her. In the social appraisal mechanism, in addition to the behavioral and structural dimensions, the method for measuring the importance or influence of the decision supporters might consider other factors. For example, the expertise or interest domain of the decision supporters could be considered.
Besides, the related thresholds should be taken into consideration while extending the social based mechanisms to a bigger scaled social network. For example, in the
-100-
proposed advertisement path planning mechanism, if the mechanism would like to extend to multiple paths, the key players selection and the paths planning problems would increased the computational complexity. Only consider the nodes with transition probability higher than some threshold can exclude some nodes and speed up the computing process and increase the scalability of the mechanism.
Finally, social network based mechanisms generally investigate novel online services from many perspective, e.g. social structural and behavioral factors, personal and group characteristics, and public and private information. The impact of the different weighting methods of varied indicators in the mechanism could be further investigated. The effectiveness of designed mechanism might be improved if these indicators can be appropriately weighted.
-101-
APPENDIX
Publication List Journal Papers
1. Yung-Ming Li, Chia-Hao Lin, and Cheng-Yang Lai, "Identifying Influential Reviewers for Word-of-Mouth Marketing," Electronic Commerce Research and Applications 9 (4) (2010), 294-304, 2010. (SSCI)
2. Yung-Ming Li, Cheng-Yang Lai, and Chien-Pang Kao, "Building a Qualitative Recruitment System via SVM with MCDM Approach," Applied Intelligence 35 (1) (2011), 75-88. (SCI)
3. Yung-Ming Li, Cheng-Yang Lai, and Ching-Wen Chen, "Discovering influencers for Marketing in the Blogoshpere," Information Sciences 181 (23) (2011), 5143-5157. (SCI)
4. Yung-Ming Li, Tzu-Fong Liao, and Cheng-Yang Lai, "A Social Recommender Mechanism for Improving Knowledge Sharing in Online Forums," Information Processing & Management 48 (5) (2012), 978-994. (SSCI)
5. Yung-Ming Li, Chun-Te Wu, and Cheng-Yang Lai, "A Social Recommender Mechanism for E-Commerce: Combining Similarity, Trust, and Relationship,"
Decision Support Systems 55 (3) (2013) 740-752. (SCI)
6. Yung-Ming Li, and Cheng-Yang Lai, "A Social Appraisal Mechanism for Online Purchase Decision Support in the Micro-Blogosphere," Decision Support Systems. (Under Revision 2nd) (SCI)
Conference Papers
1. Cheng-Yang Lai, and Yung-Ming Li, “A Social Referral Mechanism on e-Marketplace,” Proc. 15th International Conference on Electronic Commerce (ICEC 2013), Turku, Finland, August 2013.
2. Yung-Ming Li, and Cheng-Yang Lai, "A Diffusing Path Planning Mechanism for Marketing Information Propagation over Social Media," Proc. 46th Hawaii International Conference on System Science (HICSS-46), Maui, Hawaii, USA, January, 2013.
3. Yung-Ming Li and Cheng-Yang Lai, "Social Support Mechanism in Micro-blogosphere," Proc. 13th International Conference on Electronic Commerce (ICEC 2011), Liverpool, UK, August, 2011.
-102-
4. Yung-Ming Li, Cheng-Yang Lai, and Ching-Wen Chen, "Identifying Bloggers with Marketing Influence in the Blogoshpere," Proc. 11th International Conference on Electronic Commerce (ICEC 2009), Taipei, Taiwan, August, 2009.
5. Yung-Ming Li, Cheng-Yang Lai, and Chia-Hao Lin, "Discovering Influential Nodes for Viral Marketing," Proc. 42th Hawaii International Conference on System Science (HICSS-42), Manoa, Hawaii, USA, January, 2009.
6. Yung-Ming Li, Cheng-Yang Lai, and Chien-Pang Kao, "Incorporate Personality Trait with Support Vector Machine to Acquire Quality Matching of Personnel Recruitment," Proc. 4th International Conference on Business and Information (BAI 2008), Seoul, Korea, July, 2008.
-103-
REFERENCES
[1] A.S. Abrahams, J. Jiao, G.A. Wang, and W. Fan, Vehicle defect discovery from social media, Decision Support Systems 54 (1) (2012), 87-97.
[2] E. Adar and L.A. Adamic, Tracking information epidemics in blogspace, in Proceedings of the 2005 IEEE/WIC/ACM International Conference on Web Intelligence, IEEE Computer Society, Washington, DC, USA, 2005.
[3] A. Agarwal and P. Bhattacharya, Augmenting Wordnet with polarity information on adjectives. in Proceedings of the 3rd International Wordnet Conference, Jeju Island, Korea, 2006
[4] M.S. Amin, B. Yan, S. Sriram, A. Bhasin, and C. Posse, Social referral: leveraging network connections to deliver recommendations, in Proceedings of the 6th ACM Conference on Recommender Systems, New York, NY, USA, 273-276, 2012.
[5] K.T. Atanassov, Intuitionistic fuzzy sets, Fuzzy Sets and Systems 20 (1) (1986), 87-96.
[6] K. Bagwell, The Economic Analysis of Advertising, Handbook of Industrial Organization Volume 3, Edward Elgar Press, Cheltenham, UK, 2007
[7] Bangalore, Analysis on the General Profile of Users on Plurk.com, InRev Systems, Available at: http://www.slideshare.net/bexdeep/plurk-analysis-4136802, Accessed February 20, 2012
[8] K. Bawa and R.W. Shoemaker, Analyzing incremental sales from a direct mail coupon promotion, Journal of Marketing 53 (3) (1989), 66-78.
[9] J. Benesty, J. Chen, Y. Huang, and I. Cohen, Pearson correlation coefficient, noise reduction in speech processing, Springer Topics in Signal Processing (2) (2009), 1-4.
[10] M. Berry, S. Dumais, and G. O’Brien, Using linear algebra for intelligent information retrieval, SIAM Review 37 (4) (1995), 573-595.
[11] BlackHatWorld.com, eBay feedback change anyone?, Available at : http://www.blackhatworld.com/blackhat-seo/ebay/483376-ebay-feedback-exchange-any one.html, Accessed: February 20, 2013
-104-
[12] S.P. Borgatti, Identifying sets of key players in a network, Computational &
Mathematical Organization Theory 12 (1) (2006), 21-34.
[13] S.P. Borgatti, The key player problem, in Proceedings of the Dynamic Social Network Modeling and Analysis: Workshop Summary and Papers, ed. by R. Breiger, K. Carley, and P. Pattison. Washington, DC: Committee on Human Factors, National Research Council, 2003
[14] D. Boyd and N.B. Ellison, Social network sites: Definition, history, and scholarship.
Journal of Computer-Mediated Communication 13 (1) (2008), 210-230.
[15] D. Brown and N. Hayes, Chapter 20 - Making Influencer Marketing Work for Your Company, Influencer Marketing, Butterworth-Heinemann, Oxford, 212-223, 2008.
[16] J. Brown and J. Morgan, Reputation in Online Markets: Some Negative Feedback, Department of Agricultural and Resource Economics, University of California, Berkeley, CA , 2006
[17] J. Cadzow, SVD representation of unitarily invariant matrices, IEEE Transactions on Acoustics, Speech and Signal Processing 32 (3) (1984), 512-516.
[18] Y.B. Chen and J.H. Xie, Third-party product review and firm marketing strategy, Marketing Science 24 (2) (2005), 218-240.
[19] Y. Chen, F.S. Tsai, and K.L. Chan, Machine learning techniques for business blog search and mining, Expert Systems with Applications 35 (3) (2008), 581-590.
[20] J.A. Chevalier and D. Mayzlin, The effect of word of mouth on sales: Online book reviews, Journal of Marketing Research 43 (3) (2006), 345-354.
[21] Y. Cho, J. Hwang, and D. Lee, Identification of effective opinion leaders in the diffusion of technological innovation: A social network approach, Technological Forecasting and Social Change 79 (1) (2012), 97-106.
[22] J.Y. Cho, K.S. Kwon, and Y.T. Park, Q-rater: A collaborative reputation system based on source credibility theory, Expert Systems with Applications 36 (2-2) (2009), 3751-3760.
[23] S.Y. Chou, Y.H. Chang, and C.Y. Shen, A fuzzy simple additive weighting system under group decision-making for facility location selection with objective/subjective attributes,
-105-
European Journal of Operational Research 189 (1) (2008), 132-145.
[24] O.S. Dalgard, Social support - Definition and scope, Available at:
http://www.euphix.org/object_document/o5479n27411.html, Accessed April 10, 2012 [25] S.K. De, R. Biswas, and A.R. Roy, Some operations on intuitionistic fuzzy sets, Fuzzy
Sets and Systems 114 (3) (2000), 477-484.
[26] S. Deerwester, S. Dumais, G. Furnas, T. Landauer, and R. Harshman, Indexing by latent semantic analysis, Journal of the American Society for Information Science 41 (6) (1990), 391-407.
[27] C. Dellarocas, The digitization of word of mouth: Promise and challenges of online feedback mechanisms, Management Science 49 (10) (2003), 1407-1424.
[28] F. Dini and G. Spagnolo, Buying reputation on eBay: Do recent changes help?, International Journal of Electronic Business 7 (6) (2009), 581-598.
[29] W. Duan, B. Gu, and A.B. Whinston, Do online reviews matter? - An empirical investigation of panel data. Decision Support Systems 45 (4) (2008), 1007-1016.
[30] R. Dunbar, How Many Friends Does One Person Need? Dunbar’s Number and Other Evolutionary Quirks, Faber and Faber, 2010
[31] M. Eisend, Source credibility dimensions in marketing communication - A generalized solution, Journal of Empirical Generalizations in Marketing 10 (2006), 1-33.
[32] R. Garg, M.D. Smith, and R. Telang, Measuring information diffusion in an online community, Journal of Management Information Systems 28 (2) (2011), 11-38.
[33] Gartner Says, Percent of Social Media Reviews to Be Fake, Paid for By Companies, Available at: http://www.gartner.com/newsroom/id/2161315, Accessed: December 20, 2012
[34] E. Gilbert and K. Karahalios, Predicting tie strength with social media, in Proceedinsg of the SIGCHI Conference on Human Factors in Computing Systems, ACM, New York, NY, USA, 211-220, 2009.
[35] D. Gleich and L. Zhukov, SVD based term suggestion and ranking system, in Proceedings of the 4th IEEE International Conference on Data Mining, 391-394, 2004.
-106-
[36] D. Godes and D. Mayzlin, Using online conversations to study word-of-mouth communication, Marketing Science 23 (4) (2004), 545-560.
[37] L.A. Goodman, Snowball sampling, Annals of Mathematical Statistics 32 (1) (1961), 148-170.
[38] M. Granovetter, The strength of weak ties, American Journal of Sociology 78 (6) (1973), 1360-1380.
[39] R.A.R. Gurung, Coping and Social Support, Health Psychology: A Cultural Approach, Thomson Wadsworth, Belmont, CA, 2006, 131-171.
[40] J. Ha, S.H. Kwon, S.W. Kim, C. Faloutsos, and S. Park, Top-N recommendation through belief propagation, in Proceedings of the 21st ACM International Conference on Information and Knowledge Management, New York, NY, USA, 2012, 2343-2346.
[41] A. Hall and B. Wellman, Social networks and social support. in S. Cohen and S.L. Syme (eds), Social Support and Health, Orlando, Florida: Academic Press, 1985, 23-41.
[42] R.E. Hanneman, Introduction to Social Network Methods, Online Textbook Supporting Sociology, 2000, Riverside, CA: University of California.
[43] D. Hawkins, R. Best, and K. Coney, Consumer Behavior: Building Marketing Strategy, McGraw-Hill, Boston, USA, 2004.
[44] C.J. Hinde, R.S. Patching, and S.A. McCoy, Semantic transfer and contradictory evidence in intuitionistic fuzzy sets, in: Proceedings of the 2008 IEEE International Conference on Fuzzy Systems, Hong Kong, 2008, 2095-2102.
[45] J.S. House, Work Stress and Social Support, Addison-Wesley, Reading, Mass, 1981 [46] C.L. Hsu and C.C. Lin, Acceptance of blog usage: The roles of technology acceptance,
social influence and knowledge sharing motivation, Information & Management 45 (1) (2008), 65-74.
[47] Y.C. Hu, Classification performance evaluation of single-layer perceptron with Choquet integral-based TOPSIS, Applied Intelligence 29 (3) (2008), 204-215.
[48] B.A. Huberman, D.M. Romero, and F. Wu, Social networks that matter twitter under the microscope, First Monday, 14 (1-5) (2009).
-107-
[49] A.M. Hughes, Strategic Database Marketing - The Master Plan for Starting and Managing a Profitable, Customer-based Marketing Program, McGraw-Hill Professional (2005).
[50] A.M. Hughes, Strategic Database Marketing, Probus Publishing, Chicago (1994).
[51] G. Iyer, D. Soberman, and J.M. Villas-Boas, The targeting of advertising, Marketing Science 24 (3) (2005), 461-476
[52] A. Jackson, J. Yates, and W. Orlikowski, Corporate blogging: Building community through persistent digital talk, in Proceedings of the 40th Annual Hawaii International Conference on System Sciences, Washington, DC, USA, 2007.
[53] B.J. Jansen, M. Zhang, K. Sobel, and A. Chowdury, Twitter power: Tweets as electronic word of mouth, Journal of the American Society for Information Science and Technology 60 (11) (2009), 2169-2188.
[54] A. Java, X. Song, T. Finin, and B. Tseng, Why we twitter: Understanding microblogging usage and communities, in: Proceedings of the Workshop on Web Mining and Social Network Analysis, ACM, New York, NY, USA, 2007, 56-65.
[55] A. Josang, R. Ismail, and C. Boyd, A survey of trust and reputation systems for online service provision, Decision Support Systems 43 (2) (2007), 618-644.
[56] J. Kamps, M. Marx, R.J. Mokken, and M.D. Rijke, Using Wordnet to measure semantic orientation of adjectives, in: Proceedings of the LREC, 4th International Conference on Language Resources and Evaluation, 1115-1118, 2004.
[57] H. Kautz, B. Selman, and M. Shah, Referral Web: Combining social networks and collaborative filtering, Communications of the ACM 40 (3) (1997), 63-65.
[58] P. Kazienko and M. Adamski, AdROSA - Adaptive personalization of web advertising, Information Sciences 177 (11) (2007), 2269-2295.
[59] D. Kempe, J. Kleinberg, and E. Tardos, Maximizing the spread of influence through a social network, in Proceedings of the 9th ACM SIGKDD international conference on knowledge discovery and data mining, Washington, DC, USA, 2003.
[60] M. Kilduff, The friendship network as a decision-making resource: Dispositional
-108-
moderators of social influences on organizational choice, Journal of Personality and Social Psychology 62 (1) (1992), 168-180.
[61] A.J. Kim and E. Ko, Do social media marketing activities enhance customer equity? An empirical study of luxury fashion brand, Journal of Business Research 65 (10) (2012), 1480-1486.
[62] C. Kiss and M. Bichler, Identification of influencers - Measuring influence in customer networks, Decision Support Systems 46 (1) (2008), 233-253.
[63] A. Kontostathis and W.M. Pottenger, A framework for understanding Latent Semantic Indexing (LSI) performance, Information Processing & Management 42 (1) (2006), 56-73.
[64] P. Kotler and G. Zaltman, Social marketing: An approach to planned social change, Journal of Marketing 35 (3) (1971), 3-12.
[65] M.S. Kuo, G.H. Tzeng, and W.C. Huang, Group decision-making based on concepts of ideal and anti-ideal points in a fuzzy environment, Mathematical and Computer Modelling 45 (3-4) (2007), 324-339.
[66] K.S. Kwon, J.H. Cho, and Y.T. Park, Multidimensional credibility model for neighbor selection in collaborative recommendation, Expert Systems with Applications 36 (3-2) (2009), 7114-7122.
[67] L.I. Labrecque, E. Markos, and G.R. Milne, Online personal branding: Processes, challenges, and implications, Journal of Interactive Marketing 25 (1) (2011), 37-50.
[68] M. Laroche, M.R. Habibi, M.O. Richard, and R. Sankaranarayanan, The effects of social media based brand communities on brand community markers, value creation practices, brand trust and brand loyalty, Computers in Human Behavior 28 (5) (2012), 1755-1767 [69] M.K.O. Lee and E. Turban, A trust model for consumer Internet shopping, International
Journal of Electronic Commerce 6 (1) (2001), 75-91.
[70] J. Leskovec, L.A. Adamic, and B.A. Huberman, The dynamics of viral marketing, in Proceedings of the 7th ACM conference on Electronic Commerce, New York: ACM Press, 2006.
-109-
[71] D.Z. Levin and R. Cross, The strength of weak ties you can trust: The mediating role of trust in effective knowledge transfer, Management Science 50 (11) (2004), 1477-1490.
[72] D. Lewis and D. Bridger, The Soul of the New Consumer: Authenticity - What We Buy and Why in the New Economy, Nicholas Brearley, London, 2000.
[73] Y.M. Li and Y.L. Shiu, A diffusion mechanism for social advertising over microblogs, Decision Support Systems 54 (1) (2012), 9-22.
[74] Y.M. Li, C.Y. Lai, and C.W. Chen, Discovering influencers for marketing in the blogosphere, Information Sciences 181 (23) (2011), 5143-5157.
[75] F. Li and T.C. Du, Who is talking? An ontology-based opinion leader identification framework for word-of-mouth marketing in online social blogs, Decision Support Systems 51 (1) (2011), 190-197.
[76] Y.M. Li and C.P. Kao, TREPPS: A trust-based recommender system for peer production services, Expert Systems with Application 36 (2) (2009), 3263-3277.
[77] D.R. Libey, Libey on RFM, e-RFM.com, 1998, Available at:
http://www.e-rfm.com/libey/, Accessed: April 25, 2013
[78] D.R. Liu, C.H. Lai, and W.J. Lee, A hybrid of sequential rules and collaborative filtering for product recommendation, Information Sciences 179 (20) (2009), 3505-3519.
[79] H.W. Liu and G.J. Wang, Multi-criteria decision-making methods based on intuitionistic fuzzy sets, European Journal of Operational Research 179 (1) (2007), 220-233.
[80] Z. Liu, W. Yu, W. Chen, S. Wang, and F. Wu, Short text feature selection and classification for micro blog mining. in Proceedings of the International Conference on Computational Intelligence and Software Engineering, 2010.
[81] P. Lotich, Smart Church Management: A Quality Guide to Church Administration, CreateSpace Independent Publishing Platform, July 3, 2012
[82] Y.B. Lu, L. Zhao, and B. Wang, From virtual community members to C2C e-commerce buyers: Trust in virtual communities and its effect on consumers’ purchase intention, Electronic Commerce Research and Applications 9 (4) (2010), 346-360.
[83] P.D. Meo, A. Nocera, G. Terracina, and D. Ursino, Recommendation of similar users,
-110-
resources and social networks in a social internetworking scenario, Information Sciences 181 (7) (2011), 1285-1305.
[84] R.J. Miglautsch, Thoughts on RFM Scoring, Journal of Database Marketing 8 (1) (2000), 67-72.
[85] G.A. Miller, WordNet: An on-line lexical database, International Journal of Lexicography 3 (4) (1990), 235-312.
[86] G.A. Miller, WordNet: A lexical database for English, Communication of the ACM 38 (11) (1995), 39-41.
[87] H. Mintzberg, D. Raisinghani, and A. Theoret, The structure of “unstructured” decision processes, Administrative Science Quarterly 21 (2) (1976), 246-275.
[88] 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, ACM, New York, NY, USA, 2007.
[89] D.C. Muelle and T. Stratmann, Informative and persuasive campaigning, Public Choice 81 (1-2) (1994), 55-77.
[90] J. Murphy, Branding, Marketing Intelligence & Planning 6 (4) (1988), 4-8.
[91] P. Nelson, Information and consumer behavior, Journal of Political Economy 78 (2) (1970), 311-329.
[92] M. Newman, The structure and function of complex networks, SIAM Review 45 (2) (2003), 167-256.
[93] Nielsen, State of the media: the social media report q3 2011, Available at:
http://blog.nielsen.com/nielsenwire/social/, Accessed in: May 30, 2012
[94] Nielsen: Global Consumers' Trust in 'Earned' Advertising Grows in Importance,
Available at:
http://www.nielsen.com/us/en/insights/press-room/2012/nielsen-global-consumers-trust-in-earned-advertising-grows.html, Accessed: January 27, 2013
[95] NLProcessor, Text Analysis Toolkit, 2000, Available at:
http://www.infogistics.com/textanalysis.html.
-111-
[96] P. O’Reilly, Methodological issues in social support and social network research, Social Science and Medicine 26 (8) (1988), 63-73.
[97] D.L. Olson, and D. Delen, Advanced Data Mining Techniques, Springer, 1st edition, 2008.
[98] J.P. Onnela, J. Saramaki, J. Hyvonen, G. Szabo, D. Lazer, K. Kaski, J. Kertesz, and A.L.
Barabasi, Structure and tie strengths in mobile communication networks, in Proceeding of National Academy of Science of the United States of America 104 (18) (2007), 7332-7336.
[99] D.H. Park and S. Kim, The effects of consumer knowledge on message processing of electronic word-of-mouth via online consumer reviews, Electronic Commerce Research and Applications 7 (4) (2008), 399-410.
[100] C. Pornpitakpan, The Persuasiveness of Source Credibility: A Critical Review of Five Decades' Evidence, Journal of Applied Social Psychology 34 (2) (2004), 243-281.
[101] PwC and IAB, IAB Internet Advertising Revenue Report - An Industry Survey (2011 Full Year Results), PricewaterhouseCoopers and Interactive Advertising Bureau, April 2012
[102] S.G.B. Roberts, R. Wilson, P. Fedurek, and R.I.M. Dunbar, Individual differences and personal social network size and structure, Personality and Individual Differences 44 (4) (2008), 954-964.
[103] T. Robertson, J. Zielinski, and S. Ward, Consumer behavior. Scott, Foresman and Company (1984).
[104] B. Ruhnau, Eigenvector-centrality - a node-centrality?, Social Networks 22 (4) (2000), 357-365.
[105] G. Salton, A. Wong, and C.S. Yang, A Vector Space Model for Automatic Indexing, Communications of the ACM 18 (11) (1975), 613-620.
[106] D. Schweitzer, A note on Whitehead’s factors of source credibility, Quarterly Journal of Speech 55 (1969), 308-310.
[107] X. Shi, L.A. Adamic, and M.J. Strauss, Networks of strong ties, Physica A: Statistical
-112-
Mechanics and its Applications 378 (1) (2007), 33-47.
[108] A.J.S. Stanaland, The Referral Engine: Teaching Your Business to Market Itself, Journal of Consumer Marketing 28 (7) (2011), 550-551.
[109] W. Steegenga and S. Forge, IBM study finds social networks influence more than half of shoppers’ decision-making - even in the store. IBM Research Center, Available at:
http://www-03.ibm.com/press/uk/en/pressrelease/35340.wss#release, Accessed April 10, 2012
[110] M. Stelzner, Social Media Marketing Industry Report, Available at:
http://www.socialmediaexaminer.com/social-media-marketing-industry-report-2012/, Accessed in: May 30, 2012
[111] E. Szmidt and J. Kacprzyk, Distances between intuitionistic fuzzy sets, Fuzzy Sets and Systems 114 (3) (2000), 505-518.
[112] Y.J. Tang, C.Y. Li, and H.H. Chen, A comparison between microblog corpus and balanced corpus from linguistic and sentimental perspectives, Proceedings of AAAI Workshop on Analyzing Microtext, San Francisco, 68-73, 2011.
[113] D. Tapscott, Net gen transforms marketing, Bus. Week, Available at:
http://www.businessweek.com/technology/content/nov2008/tc20081114_882532.htm, Accessed April 9, 2012
[114] L. Terveen and D.W. McDonald, Social matching: A framework and research agenda, ACM Transactions on Computer-Human Interaction 12 (3) (2005), 401-434.
[115] P.D. Turney and M.L. Littman, Measuring praise and criticism: Inference of semantic orientation from association, ACM Transactions on Information Systems 21 (4) (2003), 315-346.
[116] T.L. Tuten, Advertising 2.0: Social Media Marketing in a Web 2 World, Praeger Publishers, Westport, 2008.
[117] K. Valck, G.H. Bruggen, and B. Wierenga, Virtual communities: A marketing
[117] K. Valck, G.H. Bruggen, and B. Wierenga, Virtual communities: A marketing