Adaptive notifications to support knowledge sharing in close-knit virtual communities

Abstract

Social web-groups where people with common interests and goals communicate, share resources, and construct knowledge, are becoming a major part of today’s organisational practice. Research has shown that appropriate support for effective knowledge sharing tailored to the needs of the community is paramount. This brings a new challenge to user modelling and adaptation, which requires new techniques for gaining sufficient understanding of a virtual community (VC) and identifying areas where the community may need support. The research presented here addresses this challenge presenting a novel computational approach for community-tailored support underpinned by organisational psychology and aimed at facilitating the functioning of the community as a whole (i.e. as an entity). A framework describing how key community processes—transactive memory (TM), shared mental models (SMMs), and cognitive centrality (CCen)—can be utilised to derive knowledge sharing patterns from community log data is described. The framework includes two parts: (i) extraction of a community model that represents the community based on the key processes identified and (ii) identification of knowledge sharing behaviour patterns that are used to generate adaptive notifications. Although the notifications target individual members, they aim to influence individuals’ behaviour in a way that can benefit the functioning of the community as a whole. A validation study has been performed to examine the effect of community-adapted notifications on individual members and on the community as a whole using a close-knit community of researchers sharing references. The study shows that notification messages can improve members’ awareness and perception of how they relate to other members in the community. Interesting observations have been made about the linking between the physical and the VC, and how this may influence members’ awareness and knowledge sharing behaviour. Broader implications for using log data to derive community models based on key community processes and generating community-adapted notifications are discussed.

This is a preview of subscription content, log in to check access.

References

  1. Ardissono L., Bosio G.: Context-dependent awareness support in open collaboration environments. User Model. User-Adapt. Interact. 22(3), 223–254 (2012)

    Article  Google Scholar 

  2. Ardissono, L., Bosio, G., Segnan, M.: An activity awareness visualization approach supporting context resumption in collaboration environments. In: Proceedings of the International Workshop on Adaptive Support for Team Collaboration ASTC2011, held in conjunction with the 19th International Conference on User Modeling, Adaptation and Personalization, UMAP 2011, pp. 5–17. Springer, Berlin (2011)

  3. Baghaei, N., Mitrovic, T.: From modelling domain knowledge to metacognitive skills: extending a constraint-based tutoring system to support collaboration. In: Proceedings of 11th International Conference on User Modeling UM2007, Corfu, Greece, pp. 217–227. Springer, Berlin (2007)

  4. Barley, S., Dutton, W., Kiesler, S., Resnick, P., Kraut, R., Yates, J.: Does CSCW need organization theory? In: Proceedings of the 2004 ACM Conference on Computer Supported Cooperative Work, pp. 122–124. ACM Press, New York (2004)

  5. Borgatti S.P., Everett M.G.: A graph-theoretic perspective on centrality. Soc. Netw. 28(4), 466–484, Elsevier (2006)

    Article  Google Scholar 

  6. Brazelton J., Gorry A.: Creating a knowledge-sharing community if you build it, will they come?. Commun. ACM 46(2), 23–25 (2003)

    Article  Google Scholar 

  7. Bretzke, H., Vassileva, J.: Motivating cooperation on peer to peer networks. In: Proceedings of 9th International Conference on User Modelling UM2003, pp. 218–227. Springer, Berlin (2003)

  8. Chakrabarti, D., Faloutsos, C.: Graph mining: laws, generators, and algorithms. ACM Comput. Surv. 38(1), article No 2, ACM (2006)

  9. Cheng, R., Vassileva, J.: User motivation and persuasion strategy for peer-to-peer communities. In: Proceedings of 38th Hawaii International Conference on System Sciences Hawaii, USA, pp. 3–6 (2005)

  10. Cheng R., Vassileva J.: Design and evaluation of an adaptive incentive mechanism for sustained educational online communities. User Model. User-Adapt. Interact. 16(3), 321–348 (2006)

    Article  Google Scholar 

  11. Cialdini R.B.: Influence: Science and Practice. Harper Collins College Publishers, New York (1993)

    Google Scholar 

  12. Davies, J., Duke, A., Sure, Y.: OntoShare: a knowledge management environment for virtual communities of practice, K-CAP ’03. In: Proceedings of the International Conference on Knowledge Capture. ACM, Sanibel Island, FL, USA (2003)

  13. De Choudhury, M., Sundaram, H., John, A., Seligmann, D.: Contextual prediction of communication flow in social networks. In: Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence WI2007, Silicon Valley, CA, USA, pp. 57–65. IEEE Computer Society, Los Alamitos (2007)

  14. Degenne A., Forse M.: Introducing social networks. Sage, London (1999)

    Google Scholar 

  15. Ester, M., Kriegel, H.-P., Xu, X. Clustering, K.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: Proceedings of 2nd International Conference on Knowledge Discovery and Data Mining, pp. 226–231. AAAI Press, Menlo Park (1996)

  16. Falkowski, T., Spiliopoulou, M.: Users in volatile communities: studying active participation and community evolution. In: Proceeding of 11th International Conference on User Modelling UM2007. LNCS, vol. 4511/2007, pp. 47–56. Springer, Berlin (2007)

  17. Falkowski, T., Barth, A., Spiliopoulou, M.: DENGRAPH: a density-based community detection algorithm. In: Proceedings of IEEE/WIC/ACM International Conference on Web Intelligence WI2007, Silicon Valley, CA, USA, pp. 112–115. IEEE Computer Society, Los Alamitos (2007)

  18. Farzan, R., DiMicco, J., Brownholtz, B.: Spreading the honey: a system for maintaining an online community. In: Proceedings of the ACM GROUP 2009 Conference, Florida, USA, pp. 31–40. ACM Press, New York (2009)

  19. Fischer G., Ostwald J.: Knowledge management: problems, promises, realities, and challenges. IEEE Intell. Syst. 16(1), 60–72 (2001)

    Article  Google Scholar 

  20. Freeman L.: Centrality in social networks: conceptual clarification. Soc. Netw. 1(3), 215–239 (1979)

    Article  Google Scholar 

  21. Freeman L.C., Borgatti S.P., White D.R.: Centrality in valued graphs: a measure of betweenness based on network flow. Soc. Netw. 13(2), 141–154 (1991)

    MathSciNet  Article  Google Scholar 

  22. Fu, Y., Xiang, R., Liu, Y., Zhang, M., Ma, S.: Finding experts using social network analysis. In: Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence WI2007, Silicon Valley, CA, USA, pp. 77–80. IEEE Computer Society, Los Alamitos (2007)

  23. Gross J., Yellen J.: Graph Theory and Its Applications. CRC Press, London (1999)

    Google Scholar 

  24. Harper, M., Frankowski, D., Drenner, S., Ren, Y., Kiesler, S., Terveen, L., Kraut, R., Riedl, J.: Talk amongst yourselves: inviting users to participate in online conversations. In: Proceedings of the 12th International Conference on Intelligent User Interfaces IUI2007, Honolulu, Hawaii, USA, pp. 62–71. ACM Press, New York (2007).

  25. Herlocker J., Konstan J., Terveen L., Riedl J.: Evaluating collaborative filtering recommender systems. ACM Trans. Inf. Syst. TOIS 22(1), 5–53 (2004)

    Article  Google Scholar 

  26. Hubscher, R., Puntambekar, S.: Modeling learners as individuals and as groups. In: Proceedings of International Conference on Adaptive Hypermedia and Adaptive Web-Based Systems AH2004, pp. 300–303. Springer, Berlin (2004)

  27. Ilgen, D.R., Hollenbeck, J.R., Johnson, M., Jundt, D.: Teams in organizations: from input–process–output models to IMOI models. Annu. Rev. Psychol. 56, 517–543 (2005)

    Google Scholar 

  28. Jameson, A.: Adaptive interfaces and agents. In: Jacko, J.A., Sears, A. (eds.) Human-Computer Interaction Handbook, pp. 305–330. Erlbaum, Mahwah, NJ (2003)

  29. Kameda T., Ohtsubo Y., Takezawa M.: Centrality in sociocognitive networks and social influence: an illustration in a group decision-making context. J. Pers. Soc. Psychol. 73(2), 296–309 (1997)

    Article  Google Scholar 

  30. Kay, J., Maisonneuve, N., Yacef, K., Reimann, P.: The big five and visualisations of team work activity. In: Proceedings of Intelligent Tutoring Systems ITS2006. LNCS, vol. 4053/2006, pp. 197–206. Springer, Berlin

  31. Khan, J., Shaikh, S.: Relationship algebra for computing in social networks and social network based applications. In: Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence WI2006, Hong Kong, pp. 113–116. IEEE Computer Society, Los Alamitos (2006)

  32. Kim, H.-N., El Saddik, A.: Exploring social tagging for personalized community recommendations. User Model. User-Adapt. Interact. (Special Issue on Personalization in Social Web Systems, Brusilovsky, P., Chin, D. eds.). Springer, Berlin (2012, this issue)

  33. Kleanthous, S., Dimitrova, V.: Detecting changes over time in a knowledge sharing community. In: Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence I2009, Milan, pp. 100–107. IEEE Computer Society, Los Alamitos (2009)

  34. Kleanthous, S., Dimitrova, V.: Analyzing community knowledge sharing behavior. In: Proceedings of User Modeling, Adaptation, and Personalization UMAP2010, pp. 231–242. Springer, Hawaii (2010)

  35. Kleanthous Loizou, S.: Intelligent support for knowledge sharing in virtual communities. Ph.D., School of Computing, University of Leeds, Leeds (2010)

  36. Kollock, P.: The economies of online cooperation: gifts and public goods in cyberspace. In: Smith, M., Kollock, P. (eds.) Communities in Cyberspace, pp. 220–239. Routledge, London (1999)

  37. Kunegis, J., Lommatzsch, A., Bauckhage, C.: The slashdot zoo: mining a social network with negative edges. In: Proceedings of 18th International Conference on World Wide Web WWW2009, Madrid, Spain, pp. 741–750. ACM Press, New York (2009)

  38. Latora V., Marchiori M.: A measure of centrality based on network efficiency. New J. Phys. 9(6), 188 (2007)

    Article  Google Scholar 

  39. Lave J., Wenger E.: Situated Learning Legitimate Peripheral Participation. Cambridge University Press, New York (1991)

    Google Scholar 

  40. Lin, Y.-R., Sundaram, H., Chi, Y., Tatemura, J., Tseng, B.: Blog community discovery and evolution based on mutual awareness expansion. In: Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence WI2007, Silicon Valley, CA, USA, pp. 48–56. IEEE Computer Society, Los Alamitos (2007)

  41. Lin, Y.-R., Chi, Y., Zhu, S., Sundaram, H., Tseng, B.: Facetnet: a framework for analyzing communities and their evolutions in dynamic networks. In: Proceeding of 17th International Conference on World Wide Web WWW2008, Beijing, China, pp. 685–694. ACM Press, New York (2008)

  42. Liu, S., Liu, F., Yu, C., Meng, W.: An effective approach to document retrieval via utilizing WordNet and recognizing phrases. In: Proceedings of the 27th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Sheffield, United Kingdom, pp. 266–272. ACM Press, New York (2004)

  43. Lo, S., Lin, C.: WMR—a graph-based algorithm for friend recommendation. In: Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence WI2006, Hong Kong, pp. 121–128. IEEE Computer Society, Los Alamitos (2006)

  44. Masthoff J.: Group modeling: selecting a sequence of television items to suit a group of viewers. User Model. User-Adapt. Interact. 14(1), 37–85 (2004)

    Article  Google Scholar 

  45. McDermott R.: Community development as a natural step: five stages of community development. Knowl. Manag. Rev. 3(5), 16–19 (2000)

    Google Scholar 

  46. Mohammed S., Dumville B.C.: Team mental models in a team knowledge framework: expanding theory and measurement across disciplinary boundaries. J. Organ. Behav. 22(2), 89–106 (2001)

    Article  Google Scholar 

  47. Nieminen J.: On the centrality in a graph. Scand. J. Psychol. 15(1), 332–336 (1974)

    Article  Google Scholar 

  48. Nonaka I., Toyama R., Konno N.: SECI, Ba and leadership a unified model of dynamic knowledge creation. Long Range Plan. 33(1), 5–34 (2000)

    Article  Google Scholar 

  49. Olson D.L., Delen D.: Advanced Data Mining Techniques. Springer, Berlin (2008)

    Google Scholar 

  50. Pal, A., Farzan, R., Konstan, J.A., Kraut, R.E.: Early detection of potential experts in question answering communities. In: Proceedings of User Modeling Adaption and Personalization UMAP2011, Girona, Spain, pp. 231–242. Springer, Berlin (2011)

  51. Paletz S., Schunn C.: A social-cognitive framework of multidisciplinary team innovation. Top. Cogn. Sci. 2(1), 73–95 (2010)

    Article  Google Scholar 

  52. Paramythis, A., Lau, L., Demetriadis, S., Tzagarakis, M., Kleanthous, S.: International Workshop on Adaptive Support for Team Collaboration ASTC2011, held in conjunction with the International Conference on User Modeling, Adaptation, and Personalization UMAP2011, Girona, Spain (2011)

  53. Phillips K.: The effects of categorically based expectations on minority influence: the importance of congruence. Pers. Soc. Psychol. Bull. 29(1), 3–13 (2003)

    Article  Google Scholar 

  54. Pierrakos D., Paliouras G.: Personalizing web directories with the aid of web usage data. IEEE Trans. Knowl. Data Eng. 22(9), 1331–1344 (2010)

    Article  Google Scholar 

  55. Pirolli, P., Kairam, S.: A knowledge-tracing model of learning from a social tagging system. User Model. User-Adapt. Interact. (Special Issue on Personalization in Social Web Systems, Brusilovsky, P., Chin, D., eds.). Springer, Berlin (2012)

  56. Preece, J.: An event-driven community in Washington, DC: Forces that influence participation. In: Foth, M.H. (ed.) Handbook of Research on Urban Informatics: The Practice and Promise of the Real-Time City, IGI Global, PA, USA, pp. 87–96 (2009)

  57. Preece, J., Maloney-Krichmar, D., Abras, C.: History and emergence of online communities. In: Wellman, B. (ed.) Encyclopedia of Community. Berkshire Publishing Group, Great Barrington (2003)

  58. Preece J., Nonnecke B., Andrews D.: The top 5 reasons for lurking: improving community experience for everyone. Comput. Hum. Behav. 2(1), 201–223 (2004)

    Article  Google Scholar 

  59. Puntambekar S.: Analyzing collaborative interactions: divergence, shared understanding and construction of knowledge. Comput. Educ. 47(3), 332–351 (2006)

    Article  Google Scholar 

  60. Rafaeli S., Barak M., Dan-Gur Y., Toch E.: QSIA—a web-based environment for learning, assessing and knowledge sharing in communities. Comput. Educ. 43(3), 273–289 (2004)

    Article  Google Scholar 

  61. Sankaranarayanan, K., Vassileva, J.: Visualizing reciprocal and non-reciprocal relationships in an online community. In: Proceedings of International Workshop on Adaptation and Personalization for Web 2.0, held in conjunction with UMAP2009, Trento, Italy (2009)

  62. Schmidt, K.: The problem with ‘awareness’: introductory remarks on ‘awareness in CSCW’. Comput. Support. Coop. Work CSCW 11(3–4), 285–298. Springer, Berlin (2002)

    Google Scholar 

  63. Seco, N., Veale, T., Hayes, J.: An intrinsic information content metric for semantic similarity in WordNet. In: Proceedings of 16th European Conference on Artificial Intelligence ECAI2004, Valencia, Spain, pp. 1089–1090. IOS Press, Amsterdam (2004)

  64. Shami, S., Yuan, C., Cosley, D., Xia, L., Gay, G.: That’s what friends are for: facilitating ’who knows what’ across group boundaries. In: Proceedings of the ACM GROUP 2007 Conference, Florida, USA, pp. 379–382. ACM Press, New York (2007)

  65. Song, X., Tseng, B., Lin, C.-Y., Sun, M.-T. (2005). ExpertiseNet: relational and evolutionary expert modeling. In: Proceedings of International Conference on User Modelling UM2005, LNCS, vol. 3538, pp. 99–108. Springer, Heidelberg

  66. Tagalakis, G., Keane, M.: How understanding novel compounds is facilitated by priming from similar, known compounds. In: Proceedings of the Annual Conference of the Cognitive Science Society, Stresa, Italy, pp. 2134–2139 (2005)

  67. Thomas-Hunt M., Ogden T., Neale M.: Who’s really sharing? Effects of social and expert status on knowledge exchange within groups. Manag. Sci. 49(4), 464–477 (2003)

    Article  Google Scholar 

  68. Tian, Y., Huang, T., Gao, W.: Algorithms of integrated student modeling in online virtual educational community. In: Proceedings of the Info-tech and Info-net, 2001 Conference, IEEE, Beijing (2001)

  69. Upton, K., Kay, J.: Narcissus: group and individual models to support small group work. In: Proceedings of 17th International Conference on User Modeling, Adaptation and Personalization UMAP2009, Trento, Italy, pp. 54–65. Springer, Berlin (2009)

  70. Uruchrutu, E., MacKinnon, L., Rist, R.: User cognitive style and interface design for personal, adaptive Learning. What to model? In: Proceedings of the International Conference on User Modeling UM2005, Edinburgh, Scotland, pp. 154–163. Springer, Berlin (2005)

  71. Varelas, G., Voutsakis, E., Raftopoulou, P., Petrakis, E., Milios, E.: Semantic similarity methods in WordNet and their application to information retrieval on the Web. In: Proceedings of the 7th Annual ACM International Workshop on Web Information and Data Management, Bremen, Germany, pp. 10–16. ACM Press, New York (2005)

  72. Viermetz, M., Skubacz, M.: Using topic discovery to segment large communication graphs for social network analysis. In: Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence WI2007, Silicon Valley, CA, USA, pp. 95–99. IEEE Computer Society, Los Alamitos (2007)

  73. Wegner, D.M.: Transactive memory: a contemporary analysis of the group mind. In: Mullen, B., Goethals, G.R. (eds) Theories of Group Behavior, pp. 185–208. Springer, Berlin (1986)

  74. Wellman B.: Physical place and cyber place: the rise of networked individualism. Int. J. Urban Reg. Res. 25(2), 227–252 (2001)

    Article  Google Scholar 

  75. Wenger E.: Communities of practice and social learning systems. Organization 7(2), 225–246 (2000)

    Article  Google Scholar 

  76. Wolfgang P., Uta P.-B., Wolfgang G., Tom G., Sabine K., Scafer L.: Presenting activity information in an inhabited information spaces. Comput. Support. Coop. Work 29(4), 181–208 (2004)

    Google Scholar 

  77. Zhang, J., Ackerman, M., Adamic, L.: Expertise networks in online communities: structure and algorithms. In: Proceedings of the International Conference on World Wide Web WWW2007, Alberta, Canada, pp. 221–230. ACM Press, New York (2007)

Download references

Author information

Affiliations

Authors

Corresponding author

Correspondence to Vania Dimitrova.

Additional information

The work reported here is based on the Ph.D. studies of the author conducted at the University of Leeds, UK.

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Kleanthous Loizou, S., Dimitrova, V. Adaptive notifications to support knowledge sharing in close-knit virtual communities. User Model User-Adap Inter 23, 287–343 (2013). https://doi.org/10.1007/s11257-012-9127-y

Download citation

Keywords

  • Community modelling
  • Adaptive support for knowledge sharing
  • Virtual communities