Advertisement

Journal of Systems Science and Systems Engineering

, Volume 22, Issue 3, pp 257–282 | Cite as

Social media research: A review

  • Junjie WuEmail author
  • Haoyan Sun
  • Yong Tan
Article

Abstract

Social media is fundamentally changing the way people communicate, consume and collaborate. It provides companies a new platform to interact with their customers. In academia, there is a surge in research efforts on understanding its effects. This paper aims to provide a review of current status of social media research. We discuss the specific domains in which the impacts of social media have been examined. A brief review of applicable research methodologies and approaches is also provided.

Keywords

social media empirical models experimental methods analytical approaches predictive analytics 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. [1]
    Ahn, Y.Y., Bagrow, J.P. & Lehmann, S. (2010). Link communities reveal multiscale complexity in networks. Nature, 466(7307): 761–764Google Scholar
  2. [2]
    Anagnostopoulos, A., Kumar, R. & Mahdian, M. (2008). Influence and correlation in social networks. Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2008: 7–15Google Scholar
  3. [3]
    Angst, C.M., Agarwal, R., Sambamurthy, V. & Kelley, K. (2010). Social contagion and information technology diffusion: the adoption of electronic medical records in U.S. hospitals. Management Science, 56(8): 1219–1241Google Scholar
  4. [4]
    Aral, S. & Walker, D. (2011). Creating social contagion through viral product design: a randomized trial of peer influence in networks. Management Science, 57(9): 1623–1639Google Scholar
  5. [5]
    Aral, S., Dellarocas, C. & Godes, D. (2013). Social media and business transformation: a framework for research. Information Systems Research, 24(1): 3–13Google Scholar
  6. [6]
    Archak, N., Ghose, A. & Ipeirotis, P.G. (2011). Deriving the pricing power of product features by mining consumer reviews. Management Science, 57(8): 1485–1509Google Scholar
  7. [7]
    Asur, S. & Huberman, B.A. (2010). Predicting the future with social media. IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT), 1: 492–499Google Scholar
  8. [8]
    Backstrom, L. & Leskovec, J. (2011). Supervised random walks: predicting and recommending links in social networks. Proceedings of the 4th ACM International Conference on Web Search and Data Mining, 2011: 635–644Google Scholar
  9. [9]
    Bakshy, E., Hofman, J.M., Mason, W.A. & Watts, D.J. (2011). Everyone's an influencer: quantifying influence on twitter. Proceedings of the 4th ACM International Conference on Web Search and Data Mining, 2011: 65–74Google Scholar
  10. [10]
    Bayus, B.L. (2013). Crowdsourcing new product ideas over time: an analysis of the dell ideastorm community. Management Science, 59(1): 226–244Google Scholar
  11. [11]
    Berger, J. & Schwartz, E.M. (2011). What drives immediate and ongoing word of mouth? Journal of Marketing Research, 48(5): 869–880.Google Scholar
  12. [12]
    Bollen, J., Mao, H. & Zeng, X. (2011). Twitter mood predicts the stock market. Journal of Computational Science, 2(1): 1–8Google Scholar
  13. [13]
    Bramoullé, Y., Djebbari, H. & Fortin, B. (2009). Identification of peer effects through social networks. Journal of Econometrics. 150(1): 41–55MathSciNetGoogle Scholar
  14. [14]
    Burke, R., Mobasher, B., Williams, C. & Bhaumik, R. (2006). Classification features for attack detection in collaborative recommendation systems. Proceedings of the 12th International Conference on Knowledge Discovery and Data Mining, 2006: 542–547Google Scholar
  15. [15]
    Burke, R., Mobasher, B., Zabicki, R. & Bhaumik, R. (2005). Identifying attack models for secure recommendation. Beyond Personalization: Workshop on Next Generation Recommender SystemsGoogle Scholar
  16. [16]
    Burtch, G. (2011). Herding behavior as a network externality. ICIS 2011 Proceedings, 2011: 28Google Scholar
  17. [17]
    Burtch, G., Ghose, A. & Wattal, S. (2013). An empirical examination of the antecedents and consequences of contribution patterns in crowd-funded markets. Information Systems Research, 24(3): 499–519Google Scholar
  18. [18]
    Calais Guerra, P.H., Veloso, A. & Meira, J.W. (2011). From bias to opinion: a transfer-learning approach to real-time sentiment analysis. Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2011:150–158Google Scholar
  19. [19]
    Chen, H., Chiang, R.H.L. & Storey, V.C. (2012). Business intelligence and analytics: from big data to big impact. MIS Quarterly, 36(4): 1165–1188Google Scholar
  20. [20]
    Chen, W., Wang, C. & Wang, Y. (2010). Scalable influence maximization for prevalent viral marketing in large-scale social networks. Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2010: 1029–1038Google Scholar
  21. [21]
    Chen, W., Yuan, Y. & Zhang, L. (2010). Scalable influence maximization in social networks under the linear threshold model. Proceedings of the 10th IEEE International Conference on Data Mining, 2010: 88–97Google Scholar
  22. [22]
    Chevalier, J.A. & Mayzlin, D. (2006). The effect of word of mouth on sales: online book reviews. Journal of Marketing Research, 43(3): 345–354Google Scholar
  23. [23]
    Chintagunta, P.K., Gopinath, S. & Venkataraman, S. (2010). The effects of online user reviews on movie box office performance: accounting for sequential rollout and aggregation across local markets. Marketing Science, 29(5): 944–957Google Scholar
  24. [24]
    Chintagunta, P., Erdem, T., Rossi, P.E. & Wedel, M. (2006). Structural modeling in marketing: review and assessment. Marketing Science, 25(6): 604–616Google Scholar
  25. [25]
    Claussen, J., Kretschmer, T. & Mayrhofer, P. (2013). The effects of rewarding user engagement: the case of facebook apps. Information Systems Research, 24(1): 186–200Google Scholar
  26. [26]
    Das, S.R. & Chen M.Y. (2007). Yahoo! for Amazon: sentiment extraction from small talk on the web. Management Science, 53: 1375–1388Google Scholar
  27. [27]
    Dawande, M., Mookerjee, V., Sriskandarajah, C. & Zhu, Y. (2012). Structural search and optimization in social networks. INFORMS Journal on Computing, 24(4): 611–623MathSciNetGoogle Scholar
  28. [28]
    Debnath, S., Ganguly, N. & Mitra, P. (2008). Feature weighting in content based recommendation system using social network analysis. Proceedings of the 17th International Conference on World Wide Web, 2008: 1041–1042Google Scholar
  29. [29]
    DiMicco, J., Millen, D.R., Geyer, W., Dugan, C., Brownholtz, B. & Muller, M. (2008). Motivations for social networking at work. Proceedings of the 2008 ACM Conference On Computer Supported Cooperative Work, 2008: 711–720Google Scholar
  30. [30]
    Domingos, P. & Richardson, M. (2001). Mining the network value of customers. Proceedings of the 7th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2001: 57–66Google Scholar
  31. [31]
    Dong, Y., Tang, J., Wu, S. & Tian, J. (2012). Link prediction and recommendation across heterogeneous social networks. Proceedings of the 12th IEEE International Conference on Data Mining, 2012: 181–190Google Scholar
  32. [32]
    Dou, Y., Niculescu, M.F. & Wu., D.J. (2013). Engineering optimal network effects via social media features and seeding in markets for digital goods and services. Information Systems Research, 24(1): 164–185Google Scholar
  33. [33]
    Ebbes, P., Wedel, M. & Böckenholt, U. (2009). Frugal IV alternatives to identify the parameter for an endogenous regressor. Journal of Applied Econometrics, 24(3): 446–468MathSciNetGoogle Scholar
  34. [34]
    Fang, X., Hu, P. J.H., Li, Z. (Lionel) & Tsai, W. (2013). Predicting adoption probabilities in social networks. Information Systems Research, 24(1): 128–145Google Scholar
  35. [35]
    Fichman, R.G., Kohli, R. & Krishnan, R. (2011). Editorial Overview — The Role of Information Systems in Healthcare: Current Research and Future Trends. Information Systems Research, 22(3): 419–428Google Scholar
  36. [36]
    Flake, G.W., Lawrence, S., Giles, C.L. & Coetzee, F.M. (2002). Self-organization and identification of web communities. Computer, 35(3): 66–70Google Scholar
  37. [37]
    Ghose, A., Goldfarb, A. & Han, S.P. (2012). How is the mobile internet different? search costs and local activities. Information Systems Research, forthcomingGoogle Scholar
  38. [38]
    Ghose, A. & Han, S.P. (2011). An empirical analysis of user content generation and usage behavior on the mobile internet. Management Science, 57(9): 1671–1691Google Scholar
  39. [39]
    Girvan, M. & Newman, M.E. (2002). Community structure in social and biological networks. Proceedings of the National Academy of Sciences, 99(12): 7821–7826MathSciNetzbMATHGoogle Scholar
  40. [40]
    Godes, D. & Mayzlin, D. (2004). Using online conversations to study word-of-mouth communication. Marketing Science, 23(4): 545–560Google Scholar
  41. [41]
    Godes, D. & Mayzlin, D. (2009). Firm-created word-of-mouth communication: evidence from a field test. Marketing Science, 28(4): 721–739Google Scholar
  42. [42]
    Godes, D. & Silva, J.C. (2012). Sequential and temporal dynamics of online opinion. Marketing Science, 31(3): 448–473Google Scholar
  43. [43]
    Goh, K.Y., Heng, C.S. & Lin, Z. (2013). Social media brand community and consumer behavior: quantifying the relative impact of user- and marketer-generated content. Information Systems Research, 24(1): 88–107Google Scholar
  44. [44]
    Gopinath, S., Chintagunta, P.K. & Venkataraman, S. (2013). Blogs, advertising, and local-market movie box office performance. Management Science, forthcomingGoogle Scholar
  45. [45]
    Goyal, A., Bonchi, F. & Lakshmanan, L. (2010). Learning influence probabilities in social networks. Proceedings of the 3rd ACM International Conference on Web Search and Data Mining, 2010: 241–250Google Scholar
  46. [46]
    Granovetter, M.S. (1973). The strength of weak ties. American Journal of Sociology, 1973: 1360–1380Google Scholar
  47. [47]
    Gu, B., Park, J. & Konana, P. (2012). Research note — the impact of external word-of-mouth sources on retailer sales of high-involvement products. Information Systems Research, 23(1): 182–196Google Scholar
  48. [48]
    Hildebrand, C., Häubl, G., Herrmann, A. & Landwehr, J.R. (2013). When social media can be bad for you: community feedback stifles consumer creativity and reduces satisfaction with self-designed products. Information Systems Research, 24(1): 14–29Google Scholar
  49. [49]
    Ho, C., Wu, J. & Tan, Y. (2013). Disconfirmation effect on online opinion expression. Working paperGoogle Scholar
  50. [50]
    Hoff, P.D., Raftery, A.E. & Handcock, M.S. (2002). Latent space approaches to social network analysis. Journal of the American Statistical Association, 97(460): 1090–1098MathSciNetzbMATHGoogle Scholar
  51. [51]
    Hosanagar, K., Han, P. & Tan, Y. (2010). Diffusion models for peer-to-peer (p2p) media distribution: on the impact of decentralized, constrained supply. Information Systems Research, 21(2): 271–287Google Scholar
  52. [52]
    Ienco, D., Bonchi, F. & Castillo, C. (2010). The meme ranking problem: Maximizing microblogging virality. IEEE International Conference on Data Mining Workshops, 2010: 328–335Google Scholar
  53. [53]
    Iyengar, R., Van den Bulte, C. & Valente, T.W. (2011). Opinion leadership and social contagion in new product diffusion. Marketing Science, 30(2): 195–212Google Scholar
  54. [54]
    Jamali, M. & Ester, M. (2010). A matrix factorization technique with trust propagation for recommendation in social networks. Proceedings of the 4th ACM Conference on Recommender Systems, 2010: 135–142Google Scholar
  55. [55]
    Jing, X. & Xie, J. (2011). Group buying: a new mechanism for selling through social interactions. Management Science, 57(8): 1354–1372Google Scholar
  56. [56]
    Kempe, D., Kleinberg, J. & Tardos, É. (2003). Maximizing the spread of influence through a social network. Proceedings of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2003:137–146Google Scholar
  57. [57]
    Kernighan B.W. & Lin S. (1970). An efficient heuristic procedure for partitioning graphs. Bell Systems Technical Journal, 49: 291–307zbMATHGoogle Scholar
  58. [58]
    Kimura, M. & Saito, K. (2006). Tractable models for information diffusion in social networks. Knowledge Discovery in Databases: PKDD 2006. Springer Berlin Heidelberg, 2006: 259–271Google Scholar
  59. [59]
    Kleemann, F., Voß, G.G. & Rieder, K. (2008). Un(der)paid Innovators: the commercial utilization of consumer work through crowdsourcing. Science, Technology & Innovation Studies, 4(1): 5–26Google Scholar
  60. [60]
    Konstas, I., Stathopoulos, V. & Jose, J.M. (2009). On social networks and collaborative recommendation. Proceedings of the 32th ACM SIGIR International Conference on Research and Development in Information Retrieval, 2009: 195–202Google Scholar
  61. [61]
    Krackhardt, D. (1992). The strength of strong ties: The importance of philos in organizations. Networks and Organizations: Structure, Form, and Action, 1992: 216–239Google Scholar
  62. [62]
    Kumar, V., Bhaskaran, V., Mirchandani, R. & Shah, M. (2013). Practice prize winner-creating a measurable social media marketing strategy: increasing the value and ROI of intangibles and tangibles for hokey pokey. Marketing Science, 32(2): 194–212Google Scholar
  63. [63]
    Kwak, H., Lee, C., Park, H. & Moon, S. (2010). What is twitter, a social network or a news media? Proceedings of the 19th ACM International Conference on World Wide Web, 2010: 591–600Google Scholar
  64. [64]
    Lam, S. & Riedl, J. (2004). Shilling recommender systems for fun and profit. Proceedings of the 13th International Conference on World Wide Web, 2004: 309–402Google Scholar
  65. [65]
    Lancichinetti, A., Fortunato, S. & Kertész, J. (2009). Detecting the overlapping and hierarchical community structure in complex networks. New Journal of Physics, 11(3): 033015Google Scholar
  66. [66]
    Lee, M., Kim, M. & Peng, W. (2013). Consumer reviews: reviewer avatar facial expression and review valence. Internet Research, 23(2): 116–132Google Scholar
  67. [67]
    Leskovec, J., Krause, A., Guestrin, C. & Faloutsos, C. (2007). Cost-effective outbreak detection in networks. Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2007: 420–429Google Scholar
  68. [68]
    Lin, M., Prabhala, N.R. & Viswanathan, S. (2013). Judging borrowers by the company they keep: friendship networks and information asymmetry in online peer-to-peer lending. Management Science, 59(1): 17–35Google Scholar
  69. [69]
    Lin, Y.R., Chi, Y., Zhu, S., Sundaram, H. & Tseng, B.L. (2009). Analyzing communities and their evolutions in dynamic social networks. ACM Transactions on Knowledge Discovery from Data, 3(2): 8Google Scholar
  70. [70]
    Liu, Y. (2006). Word of mouth for movies: its dynamics and impact on box office revenue. Journal of Marketing, 70(3): 74–89Google Scholar
  71. [71]
    Luo, X., Zhang, J. & Duan, W. (2013). Social media and firm equity value. Information Systems Research, 24(1): 146–163Google Scholar
  72. [72]
    Manski, C.F. (1993). Identification of endogenous social effects: the reflection problem. The Review of Economic Studies, 60(3): 531–542.MathSciNetzbMATHGoogle Scholar
  73. [73]
    Mayzlin, D. & Yoganarasimhan, H. (2012). Link to success: how blogs build an audience by promoting rivals. Management Science 58(9): 1651–1668Google Scholar
  74. [74]
    Mehta, B. & Nejdl, W. (2009). Unsupervised strategies for shilling detection and robust collaborative filtering. User Modeling and User-Adapted Interaction, 19(1–2): 65–97Google Scholar
  75. [75]
    Melville, P., Gryc, W. & Lawrence, R.D. (2009). Sentiment analysis of blogs by combining lexical knowledge with text classification. Proceedings of the 5th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2009: 1275–1284Google Scholar
  76. [76]
    Miller, A.R. & Tucker, C. (2013). Active social media management: the case of health care. Information Systems Research, 24(1): 52–70Google Scholar
  77. [77]
    Mobasher, B., Burke, R., Bhaumik, R. & Williams, C. (2007). Toward trustworthy recommender systems: an analysis of attack models and algorithm robustness. ACM Transactions on Internet Technology, 7(4): 1–40Google Scholar
  78. [78]
    Moe, W.W. & Schweidel, D.A. (2012). Online product opinions: incidence, evaluation, and evolution. Marketing Science, 31(3): 372–386Google Scholar
  79. [79]
    Narayanam, R. & Narahari, Y. (2010). A shapley value-based approach to discover influential nodes in social networks. IEEE Transactions on Automation Science and Engineering, 2010(99): 1–18Google Scholar
  80. [80]
    Nepusz, T., Petróczi, A., Négyessy, L. & Bazsó, F. (2008). Fuzzy communities and the concept of bridgeness in complex networks. Physical Review E, 77(1): 016107.MathSciNetGoogle Scholar
  81. [81]
    Newman, M.E. (2004). Fast algorithm for detecting community structure in networks. Physical review E, 69(6): 066133Google Scholar
  82. [82]
    Newman, M.E. (2006). Modularity and community structure in networks. Proceedings of the National Academy of Sciences, 103(23): 8577–8582Google Scholar
  83. [83]
    O'Mahony, M., Hurley, N., Kushmerick, N. & Silvestre, G. (2004). Collaborative recommendation: a robustness analysis. ACM Transactons on Internet Technology, 4(4): 344–377Google Scholar
  84. [84]
    Oestreicher-Singer, G. & Sundararajan, A. (2012). The visible hand? demand effects of recommendation networks in electronic markets. Management Science, 58(11): 1963–1981Google Scholar
  85. [85]
    Oh, W. & S. Jeon. (2007). Membership herding and network stability in the open source community: the ising perspective. Management Science, 53(7): 1086–1101zbMATHGoogle Scholar
  86. [86]
    Palla, G., Derényi, I., Farkas, I. & Vicsek, T. (2005). Uncovering the overlapping community structure of complex networks in nature and society. Nature, 435(7043): 814–818Google Scholar
  87. [87]
    Pandit, S., Yang, Y. & Chawla, N.V. (2012). Maximizing information spread through influence structures in social networks. Proceedings of the 12th IEEE International Conference on Data Mining Workshops, 2012: 258–265Google Scholar
  88. [88]
    Pang, B., Lee, L. & Vaithyanathan, S. (2002). Thumbs up?: sentiment classification using machine learning techniques. Conference on Empirical methods in natural language processing, 10: 79–86Google Scholar
  89. [89]
    Rabe-Hesketh, S. & Skrondal, A. (2004). Generalized latent variable modeling: Multilevel, longitudinal, and structural equation models. Boca Raton: CRCGoogle Scholar
  90. [90]
    Read, J. (2005). Using emoticons to reduce dependency in machine learning techniques for sentiment classification. Proceedings of the ACL Student Research Workshop, 2005:43–48Google Scholar
  91. [91]
    Rishika, R., Kumar, A., Janakiraman, R. & Bezawada, R. (2013). The effect of customers' social media participation on customer visit frequency and profitability: an empirical investigation. Information Systems Research, 24(1): 108–127Google Scholar
  92. [92]
    Saez-Trumper, D., Comarela, G., Almeida, V., Baeza-Yates, R. & Benevenuto, F. (2012). Finding trendsetters in information networks. Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2012: 1014–1022Google Scholar
  93. [93]
    Saito, K., Kimura, M., Ohara, K. & Motoda, H. (2010). Selecting information diffusion models over social networks for behavioral analysis. Machine Learning and Knowledge Discovery in Databases. Springer Berlin Heidelberg, 2010: 180–195.Google Scholar
  94. [94]
    Sasidharan, S., Santhanam, R., Brass, D.J. & Sambamurthy, V. (2012). The effects of social network structure on enterprise systems success: a longitudinal multilevel analysis. Information Systems Research, 23(3): 658–678Google Scholar
  95. [95]
    Schafer, J.B., Frankowski, D., Herlocker, J. & Sen, S. (2007). Collaborative filtering recommender systems. Springer Berlin Heidelberg, 2007: 291–324Google Scholar
  96. [96]
    Schwienbacher, A. & Larralde, B. (2010). Crowdfunding of small entrepreneurial ventures. Handbook of entrepreneurial finance, forthcomingGoogle Scholar
  97. [97]
    Shriver, S.K., Nair, H.S. & Hofstetter, R. (2013). Social ties and user-generated content: evidence from an online social network. Management Science, 59(6): 1425–1443Google Scholar
  98. [98]
    Singh, P.V., Tan, Y. & Youn, N. (2011). A Hidden Markov Model of Developer Learning Dynamics in Open Source Software Projects. Information Systems Research, 22(4): 790–807Google Scholar
  99. [99]
    Slater, P.B. (2008). Established clustering procedures for network analysis. arXiv preprint arXiv:0806.4168Google Scholar
  100. [100]
    Snijders, T.A.B. (2006). Statistical methods for network dynamics. Proceedings of the XLIII Scientific Meeting, 2006(1994): 281–296Google Scholar
  101. [101]
    Song, X., Chi, Y., Hino, K. & Tseng, B.L. (2007). Information flow modeling based on diffusion rate for prediction and ranking. Proceedings of the 16th International Conference on World Wide Web, 2007: 191–200Google Scholar
  102. [102]
    Sonnier, G.P., McAlister, L. & Rutz, O.J. (2011) A dynamic model of the effect of online communications on firm sales. Marketing Science, 30(4): 702–716Google Scholar
  103. [103]
    Stonebraker, M. (2010). SQL databases v. NoSQL databases. Communications of the ACM, 53(4): 10–11Google Scholar
  104. [104]
    Sun, M. (2012). How does the variance of product ratings matter? Management Science, 58(4): 696–707Google Scholar
  105. [105]
    Susarla, A., Oh, J.H. & Tan, Y. (2012). Social networks and the diffusion of user-generated content: evidence from youtube. Information Systems Research, 23(1): 23–41Google Scholar
  106. [106]
    Symeonidis, P., Nanopoulos, A. & Manolopoulos, Y. (2008). Providing justifications in recommender systems. IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans, 38(6): 1262–1272Google Scholar
  107. [107]
    Tan, P.N., Steinbach, M. & Kumar, V. (2005). Introduction to Data Mining. Addison-WesleyGoogle Scholar
  108. [108]
    Tang, J., Sun, J., Wang, C. & Yang, Z. (2009). Social influence analysis in large-scale networks. Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2009: 807–816Google Scholar
  109. [109]
    Tang, L., Liu, H. & Zhang, J. (2012). Identifying evolving groups in dynamic multimode networks. IEEE Transactions on Knowledge and Data Engineering, 24(1): 72–85Google Scholar
  110. [110]
    Toubia, O. & Stephen, A.T. (2013). Intrinsic vs. Image-related utility in social media: why do people contribute content to twitter? Marketing Science, 32(3): 368–392Google Scholar
  111. [111]
    Trusov, M., Bodapati, A.V. & Bucklin, R.E. (2010). Determining influential users in internet social networks. Journal of Marketing Research, 47(4): 643–658Google Scholar
  112. [112]
    Wang, X., Wei, F., Liu, X., Zhou, M. & Zhang, M. (2011). Topic sentiment analysis in twitter: a graph-based hashtag sentiment classification approach. Proceedings of the 20th ACM International Conference on Information and Knowledge Management, 1031–1040Google Scholar
  113. [113]
    Wasserman, S. & Faust, K. (1994). Social network analysis: methods and applications. Cambridge University PressGoogle Scholar
  114. [114]
    Weng, J., Lim, E.P., Jiang J. & He, Q. (2010). Twitterrank: finding topic-sensitive influential twitterers. Proceedings of the 3rd ACM International Conference on Web Search and Data Mining, 2010: 261–270Google Scholar
  115. [115]
    White, T. (2012). Hadoop: the definitive guide. O'ReillyGoogle Scholar
  116. [116]
    Williams, C. (2006). Profile injection attack detection for securing collaborative recommender systems. Technical Report, DePaul UniversityGoogle Scholar
  117. [117]
    Wu, L. (2013). Social network effects on productivity and job security: evidence from the adoption of a social networking tool. Information Systems Research, 24(1): 30–51Google Scholar
  118. [118]
    Wu, Z., Cao, J., Wu, J., Wang, Y. & Liu, C. (2013). Detecting genuine communities from large-scale social networks: a pattern-based method. The Computer Journal, forthcomingGoogle Scholar
  119. [119]
    Wu, Z., Wu, J., Cao, J. & Tao D. (2012). HySAD: a semi-supervised hybrid shilling attack detector for trustworthy product recommendation. Proceedings of the 18th ACM SIGKDD International Conference On Knowledge Discovery And Data Mining, 2012: 985–993Google Scholar
  120. [120]
    Yan, L., Peng, J. & Tan, Y. (2013). Network dynamics: how do we find patients like us? Working paperGoogle Scholar
  121. [121]
    Yan, L. & Tan, Y. (2013). Feel blue so go online: an empirical study of online social support among patients. Working paperGoogle Scholar
  122. [122]
    Yang, T., Chi, Y., Zhu, S., Gong, Y. & Jin, R. (2009). A bayesian approach toward finding communities and their evolutions in dynamic social networks. In SDM, 2009: 990–1001Google Scholar
  123. [123]
    Ye, M., Yin, P. & Lee, W.C. (2010). Location recommendation for location-based social networks. Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems, 2010: 458–461Google Scholar
  124. [124]
    Zeng, X. & Wei, L. (2013). Social ties and user content generation: evidence from flickr. Information Systems Research, 24(1): 71–87Google Scholar
  125. [125]
    Zhang, J. & Liu, P. (2012). Rational herding in microloan markets. Management Science, 58(5): 892–912Google Scholar
  126. [126]
    Zhang, S., Wang, R.S. & Zhang, X.S. (2007). Uncovering fuzzy community structure in complex networks. Physical Review E, 76(4): 046103Google Scholar
  127. [127]
    Zhang, X. & Zhu, F. (2011). Group size and incentives to contribute: a natural experiment at chinese wikipedia. American Economic Review, 101(4): 1601–1615Google Scholar
  128. [128]
    Zhao, J., Dong, L., Wu, J. & Xu, K. (2012) Moodlens: an emoticon-based sentiment analysis system for chinese tweets. Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2012: 1528–1531Google Scholar
  129. [129]
    Zhao, J., Lui, J., Towsley, D., Guan, X. & Wang, P. (2013). Social sensor placement in large scale networks: a graph sampling perspective. arXiv preprint arXiv:1305.6489Google Scholar
  130. [130]
    Zhao, Y., Yang, S., Narayan, V. & Zhao, Y. (2013). Modeling consumer learning from online product reviews. Marketing Science, 32(1): 153–169Google Scholar

Copyright information

© Systems Engineering Society of China and Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.School of Economics and ManagementBeihang UniversityBeijingChina
  2. 2.Michael G. Foster School of BusinessUniversity of WashingtonSeattleUSA

Personalised recommendations