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The Trajectory of Current and Future Knowledge Market Research: Insights from the First KredibleNet Workshop

  • Sorin Adam Matei
  • Brian Britt
  • Elisa Bertino
  • Jeremy Foote
Chapter
Part of the Computational Social Sciences book series (CSS)

Abstract

To this point, no collaborative research environment has been remotely effective at allowing scholars interested in researching trust, authority, authorship, and roles on social media knowledge markets to seamlessly transition between engaging other researchers, uploading and analyzing data, and reporting findings for the world to observe. Theories that continue, rather than ignore, social and mass media theory, are yet to be developed. In our increasingly global society, with its growing emphasis on collaborative work, this is a critical gap in the research process. This chapter proposes a carefully designed approach and framework that would more actively listen to the needs of the research community in order to offer the perspective, resources, and tools that would best allow them to address the most important research problems of today and of tomorrow in the fields of authorship, roles, and credibility. Based upon present and future research needs expressed by a panel of leading researchers attending the KredibleNet workshop at Purdue University in April 2014, the authors propose the development of a new approach that problematizes the lack of theoretical coherence of current work, that advocates for strong multidisciplinary collaboration, for funding of nonintuitive research agendas and for the development of collaborative platforms that would support scholars at all stages of the scholarly process.

Keywords

Knowledge markets Data management Collaboration Theoretical frameworks Machine learning Research directions 

References

  1. 37signals, LLC. (2013). Project management software, online collaboration: Basecamp. https://basecamp.com. Accessed 8 Nov 2013.
  2. Adamic, L. A., Wei, X., Yang, J., Gerrish, S., Nam, K. K., & Clarkson, G. S. (2010). Individual focus and knowledge contribution. Accessed 1 Jan 2014.First Monday, 15(3). http://firstmonday.org/ojs/index.php/fm/article/view/2841/2475. Accessed 1 Jan 2014.
  3. Alfresco Software, Inc. (2013). Document management | Open source collaboration software | Alfresco. http://www.alfresco.com. Accessed 8 Nov 2013.
  4. Bakshy, E., Rosenn, I., Marlow, C., & Adamic, L. (2012). The role of social networks in information diffusion. In Proceedings of the 21st international conference on the World Wide Web (WWW ’12) (pp. 519–528). New York: ACM.Google Scholar
  5. Ball, J., & Ackerman, S. (2013). NSA loophole allows warrantless search for US citizens’ emails and phone calls. The Guardian. http://www.theguardian.com/world/2013/aug/09/nsa-loophole-warrantless-searches-email-calls. Accessed 1 Jan 2014.
  6. Berman, A. E., Barnett, W. K., & Mooney, S. D. (2012). Collaborative software for traditional and translational research. Human Genomics, 6, 21.CrossRefGoogle Scholar
  7. Bond, R. M., Fariss, C. J., Jones, J. J., Kramer, A. D. I., Marlow, C., Settle, J. E., & Fowler, J. H. (2012). A 61-million-person experiment in social influence and political mobilization. Nature, 489, 295–298.ADSCrossRefGoogle Scholar
  8. boyd, d., & Crawford, K. (2012). Critical questions for big data: Provocations for a cultural, technological, and scholarly phenomenon. Information, Communication & Society, 15, 662–679.CrossRefGoogle Scholar
  9. Brandes, U. (2001). A faster algorithm for betweenness centrality. Journal of Mathematical Sociology, 25, 163–177.CrossRefzbMATHGoogle Scholar
  10. Britt, B. C. (2011). System-level motivating factors for collaboration on Wikipedia: A longitudinal network analysis. (Master’s thesis). Retrieved from ProQuest Dissertations and Theses. (Accession Order No. AAI1501175).Google Scholar
  11. Cacioppo, J. T., Fowler, J. H., & Christakis, N. A. (2009). Alone in the crowd: The structure and spread of loneliness in a large social network. Journal of Personality and Social Psychology, 97, 977–991.CrossRefGoogle Scholar
  12. Christakis, N. A., & Fowler, J. H. (2007). The spread of obesity in a large social network over 32 years. New England Journal of Medicine, 357(4), 370–379.CrossRefGoogle Scholar
  13. Cornell University Library. (2013). arXiv.org e-Print archive. http://arxiv.org. Accessed 8 Nov 2013.
  14. DataONE. (2013). DataONE. http://www.dataone.org. Accessed 8 Nov 2013.
  15. DataSift. (2014). http://datasift.com/. Accessed 4 Feb 2014.
  16. Donohue, L. K. (2008). The cost of counterterrorism: Power, politics, and liberty. New York: Cambridge University Press.Google Scholar
  17. Ehrlich, K., Lin, C.-Y., & Griffiths-Fisher, V. (2007). Searching for experts in the enterprise: Combining text and social network analysis. In Proceedings of the 2007 international ACM conference on supporting group work (pp. 117–126). New York: ACM.Google Scholar
  18. FLOSSmole. (2013). FLOSSmole. http://flossmole.org. Accessed 8 Nov 2013.
  19. Gallagher, S. E., & Savage, T. (2013). Cross-cultural analysis in online community research: A literature review. Computers in Human Behavior, 29, 1028–1038.CrossRefGoogle Scholar
  20. Gao, H., Barbier, G., & Goolsby, R. (2011). Harnessing the crowdsourcing power of social media for disaster relief. IEEE Intelligent Systems, 26(3), 10–14.CrossRefGoogle Scholar
  21. Garber, M. (2011). The contribution conundrum: Why did Wikipedia succeed while other encyclopedias failed? http://www.niemanlab.org/2011/10/the-contribution-conundrum-why-did-wikipedia-succeed-while-other-encyclopedias-failed. Accessed 1 Jan 2014.
  22. Gelman, A., & Hill, J. (2006). Data analysis using regression and multilevel/hierarchical models (1st ed.). New York: Cambridge University Press.Google Scholar
  23. Gilbert, E., & Karahalios, K. (2009). Predicting tie strength with social media. In Proceedings of the SIGCHI conference on human factors in computing systems (CHI ’09) (p. 211). New York City: ACM.Google Scholar
  24. Google, Inc. (2013). Google Drive. http://drive.google.com. Accessed 8 Nov 2013.
  25. Granovetter, M. S. (1973). The strength of weak ties. American Journal of Sociology, 78, 1360–1380.CrossRefGoogle Scholar
  26. Greenwald, G. (2013). NSA collecting phone records of millions of Verizon customers daily. The Guardian. http://www.theguardian.com/world/2013/jun/06/nsa-phone-records-verizon-court-order. Accessed 1 Jan 2014.
  27. Hara, N., Shachaf, P., & Hew, K. F. (2010). Cross-cultural analysis of the Wikipedia community. Journal of the American Society for Information Science and Technology, 61, 2097–2108.CrossRefGoogle Scholar
  28. Hendler, J., & Golbeck, J. (2008). Metcalfe’s law, Web 2.0, and the semantic Web. Journal of Web Semantics, 6(1), 14–20.CrossRefGoogle Scholar
  29. Ho, Q., Yan, R., Raina, R., & Xing, E. P. (2012). Understanding the interaction between interests, conversations and friendships in Facebook. http://arxiv.org/pdf/1211.0028v1.pdf. Accessed 1 Jan 2014.
  30. Howison, J., Wiggins, A., & Crowston, K. (2011). Validity issues in the use of social network analysis with digital trace data. Journal of the Association for Information Systems, 12(12), 2.Google Scholar
  31. Huang, C. M., Chan, E., & Hyder, A. A. (2010). Web 2.0 and Internet social networking: A new tool for disaster management? Lessons from Taiwan. BMC Medical Informatics and Decision Making, 10, 57.CrossRefGoogle Scholar
  32. Jansen, B. J., Zhang, M. 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.CrossRefGoogle Scholar
  33. Jurafsky, D., & Martin, J. H. (2008). Speech and language processing: An introduction to natural language processing, computational linguistics, and speech recognition (2nd ed.). Englewood Cliffs: Prentice-Hall.Google Scholar
  34. Katz, J. S., & Martin, B. R. (1997). What is research collaboration? Research Policy, 26(1), 1–18.CrossRefGoogle Scholar
  35. Kerr, O. S. (2003). Internet surveillance law after the USA Patriot Act: The big brother that isn’t. Northwestern University Law Review, 97(2), 607–673.Google Scholar
  36. Klimeck, G., McLennan, M., Brophy, S. P., Adams, G. B., & Lundstrom, M. S. (2008). nanohub. org: Advancing education and research in nanotechnology. Computing in Science Engineering, 10(5), 17–23.CrossRefGoogle Scholar
  37. Kolyshkina, I., & van Rooyen, M. (2006). Text mining for insurance claim cost prediction. Lecture Notes in Computer Science, 3755, 192–202.CrossRefGoogle Scholar
  38. Lazer, D., Pentland, A., Adamic, L., Aral, S., Barabási, A. L., Brewer, D., et al. (2009). Life in the network: The coming age of computational social science. Science, 323, 721–723.CrossRefGoogle Scholar
  39. Matei, S. A. (2014). A social network analysis “practice capital” approach to enhance the C-Span Archive with meta-communication data to support public affairs debates and data journalism. In R. X. Browning (Ed.), The C-SPAN archives: An interdisciplinary resource for discovery, learning, and engagement. West Lafayette: Purdue University Press.Google Scholar
  40. Matei, S. A., & Dobrescu, C. (2011). Wikipedia’s “Neutral Point of View”: Settling conflict through ambiguity. The Information Society, 27, 1–12.Google Scholar
  41. McDermott, J. (2013). Google takes its tracking into the real world. http://digiday.com/platforms/google-tracking/. Accessed 1 Jan 2014.
  42. McPherson, M., Smith-Lovin, L., & Cook, J. M. (2001). Birds of a feather: Homophily in social networks. Annual Review of Sociology, 27, 415–444.CrossRefGoogle Scholar
  43. Mendeley. (2014). http://www.mendeley.com/. Accessed 4 Feb 2014.
  44. Merton, R. K. (1968). The Matthew effect in science. Science, 159, 56–63.ADSCrossRefGoogle Scholar
  45. Microsoft Corporation. (2013a). Microsoft SharePoint—collaboration software—Office.com. http://office.microsoft.com/en-us/sharepoint. Accessed 8 Nov 2013.
  46. Microsoft Corporation. (2013b). Technet forums. http://social.technet.microsoft.com/Forums/en-US/home. Accessed 8 Nov 2013.
  47. Nakatoh, T., Amano, H., & Hirokawa, S. (Sept 2013). Prediction of growth rate of operating income using securities reports. Paper presented at the IIAI International Conference on Advanced Applied Informatics 2013 (IIAI AAI 2013), Matsue, Japan.Google Scholar
  48. Newman, M. E. J. (2002). Spread of epidemic disease on networks. Physical Review E, 66(1), 016128-1–016128-11.ADSCrossRefGoogle Scholar
  49. Obama, B. (2012). Consumer data privacy in a networked world: A framework for protecting privacy and promoting innovation in the global digital economy. http://www.whitehouse.gov/sites/default/files/privacy-final.pdf. Accessed 1 Jan 2014.
  50. Onnela, J.-P., & Reed-Tsochas, F. (2010). Spontaneous emergence of social influence in online systems. Proceedings of the National Academy of Sciences, 107, 18375–18380.ADSCrossRefGoogle Scholar
  51. Pfeil, U., Zaphiris, P., & Ang, C. S. (2006). Cultural differences in collaborative authoring of Wikipedia. Journal of Computer-Mediated Communication, 12(1), 88–113.CrossRefGoogle Scholar
  52. Poulin, C., Shiner, B., Thompson, P., Vepstas, Y., Young-Xu, Y., Goertzel, B. V., et al. (2013). Predicting the risk of suicide by analyzing the text of clinical notes. http://www.durkheimproject.org/results. Accessed 8 Nov 2013.
  53. R Foundation. (2013). The R project for statistical computing. http://www.r-project.org. Accessed 8 Nov 2013.
  54. Rader, E., & Wash, R. (2008). Influences on tag choices in del.icio.us. In Proceedings of the 2008 ACM conference on computer supported cooperative work (CSCW ’08) (pp. 239–248). New York: ACM.Google Scholar
  55. Raymond, E. S. (1999). The cathedral and the bazaar. Sebastopol: O’Reilly Media.Google Scholar
  56. Rosenbush, S. (2013). Facebook tests software to track your cursor on screen. Wall Street Journal. http://blogs.wsj.com/cio/2013/10/30/facebook-considers-vast-increase-in-data-collection. Accessed 1 Jan 2014.
  57. Salganik, M. J., Dodds, P. S., & Watts, D. J. (2006). Experimental study of inequality and unpredictability in an artificial cultural market. Science, 311, 854–856.ADSCrossRefGoogle Scholar
  58. Schleyer, T., Butler, B. S., Song, M., & Spallek, H. (2012). Conceptualizing and advancing research networking systems. ACM Transactions of Computer-Human Interaction, 19(1), 2.CrossRefGoogle Scholar
  59. Shneiderman, B. (2014). Building trusted social media communities: A research roadmap for promoting credible content. In S. A. Matei & E. Bertino (Eds.), Roles, trust, and reputation in social media knowledge markets: Theory and methods. New York: Springer.Google Scholar
  60. Sobkowicz, P., Kaschesky, M., & Bouchard, G. (2012). Opinion mining in social media: Modeling, simulating, and forecasting political opinions in the web. Government Information Quarterly, 29(4), 470–479.CrossRefGoogle Scholar
  61. Sohmer, S. (2011). Does Watson think a poor workman blames Yogi Berra? http://hypervocal.com/entertainment/2011/does-watson-think-a-poor-workman-blames-yogi-berra. Accessed 1 Jan 2014.
  62. Sopan, A., Rey, P. J., Ahn, J., Plaisant, C., & Shneiderman, B. (2012). The dynamics of web-based community safety groups: Lessons learned from the nation of neighbors. http://hcil2.cs.umd.edu/trs/2012-02/2012-02.pdf. Accessed 1 Jan 2014.
  63. Starbird, K., Palen, L., Hughes, A. L., & Vieweg, S. (2010). Chatter on the red. In Proceedings of the 2010 ACM conference on computer supported collaborative work (pp. 241–250). New York: ACM.Google Scholar
  64. Teng, C.-Y., Lauterbach, D., & Adamic, L. A. (June 2010). I rate you. You rate me. Should we do so publicly? Paper presented at the 3rd workshop on online social networks (WOSN ’10), Boston, MA.Google Scholar
  65. Thelwall, M., Buckley, K., & Paltoglou, G. (2011). Sentiment in Twitter events. Journal of the American Society for Information Science and Technology, 62, 406–418.CrossRefGoogle Scholar
  66. Tufekci, Z., & Wilson, C. (2012). Social media and the decision to participate in political protest: Observations from Tahrir Square. Journal of Communication, 62(2), 363–379.CrossRefGoogle Scholar
  67. Valente, T. W. (1995). Network models of the diffusion of innovations (Quantitative methods in communication subseries). Cresskill: Hampton.Google Scholar
  68. Weinberger, D. (2011). Too big to know: Rethinking knowledge now that the facts aren’t the facts, experts are everywhere, and the smartest person in the room is the room. New York: Basic Books.Google Scholar
  69. Welser, H. T., Gleave, E., Fisher, D., & Smith, M. (2007). Visualizing the signatures of social roles in online discussion groups. Journal of Social Structure, 8(2), 1–32.Google Scholar
  70. Welser, H. T., Cosley, D., Kossinets, G., Lin, A., Dokshin, F., Gay, G., & Smith, M. (2011). Finding social roles in Wikipedia. In Proceedings of the 2011 iConference (pp. 122–129). New York: ACM.Google Scholar
  71. Williford, C., Henry, C., & Friedlander, A. (2012). One culture: Computationally intensive research in the humanities and social sciences. Washington, DC: Council on Library and Information Resources.Google Scholar
  72. Wohlsen, M. (2013). The only thing Amazon has to fear is Amazon itself. http://www.wired.com/business/2013/05/amazon-dominance. Accessed 1 Jan 2014.
  73. Xie, P., & Xing, E. P. (July 2013). Integrating document clustering and topic modeling. Paper presented at the 29th International Conference on Uncertainty in Artificial Intelligence (UAI 2013), Bellevue, WA.Google Scholar
  74. Zhang, D., & Lowry, P. B. (2008). Issues, limitations, and opportunities in cross-cultural research on collaborative software in information systems. Journal of Global Information Management, 16(1), 61–92.CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Sorin Adam Matei
    • 1
  • Brian Britt
    • 1
  • Elisa Bertino
    • 1
  • Jeremy Foote
    • 1
  1. 1.Purdue UniversityWest LafayetteUSA

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