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A study of the impacts of positive/negative feedback on collective wisdom—case study on social bookmarking sites

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Abstract

The core spirit for web 2.0 is collective wisdom (i.e., the contribution of users, and the creation of value through the interaction between users). Social bookmarking sites integrate all kind of contents on the Internet (especially those generated by users), and play the role of pivot between content production and consumption. This paper mainly investigates how the positive/negative feedbacks would impact the quality of the collective wisdom within the autonomous service environments (i.e., the social bookmarking sites). Our research findings show that the performance of social bookmarking sites has a tradeoff between collective filtering (i.e., results of positive feedbacks) and front page update frequency that should be carefully managed for ensuring the good quality in collective wisdom and service performance. Moreover, the negative feedback could also shape the collective wisdom and stabilize the system performance. The research findings are believed to provide some managerial guidelines for web 2.0 sites design and operations.

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References

  • Adar, E., Zhang, L., Adamic, L. A., & Lukose, R. M. (2004). Implicit structure and the dynamics of blogspace. Workshop on the Weblogging Ecosystem, 13th International World Wide Web Conference.

  • Bonabeau, E., Theraulaz, G., Deneubourg, J. L., Aron, S., & Camazine, S. (1997). Self-organization in social insects. Trends in Ecology & Evolution, 12(5), 188–193.

    Article  Google Scholar 

  • Bonabeau, E. (2002). Agent-based modeling: Methods and techniques for simulating human systems. Proceedings of the National Academy of Sciences, 99(90003), 7280–7287.

    Article  Google Scholar 

  • Camazine, S., Deneubourg, J. L., Franks, N. R., Sneyd, J., Theraulaz, G., & Bonabeau, E. (2001) “Self-organization in biological systems. Self-organization in biological systems. Princeton University Press.

  • Hamilton, W. D. (1964). The genetical evolution of social behaviour, I and II. Journal of Theoretical Biology, 7, 1–52.

    Article  Google Scholar 

  • Hammond, T., Hannay, T., Lund, B., & Scott, J. (2005). Social bookmarking tools (I). D-Lib Magazine, 11(4), 1082–9873.

    Article  Google Scholar 

  • Heylighen, F. (1999). Collective intelligence and its implementation on the web: Algorithms to develop a collective mental map. Computational & Mathematical Organization Theory, 5(3), 253–280.

    Article  Google Scholar 

  • Lerman, K. (2007). Dynamics of collaborative document rating systems. Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 Workshop on Web Mining and Social Network Analysis, 46–55.

  • Lerman, K. (2007). Social networks and social information filtering on digg. Proceedings of the International Conference on Weblogs and Social Media (ICWSM-07)

  • Lerman, K. (2007). Social information processing in social news aggregation. IEEE Internet Computing, 16–28.

  • Lund, B., Hammond, T., Flack, M., Hannay, T., & NeoReality, I. (2005). Social bookmarking tools (II). D-Lib Magazine, 11(4), 1082–9873.

    Article  Google Scholar 

  • Macal, C. M., & North, M. J. (2005). Tutorial on agent-based modeling and simulation. Proceedings of the 37th Conference on Winter Simulation, 2–15.

  • Mika, P. (2007). Ontologies are us: A unified model of social networks and semantics. Web Semantics: Science, Services and Agents on the World Wide Web, 5(1), 5–15.

    Article  Google Scholar 

  • Railsback, S. F., Lytinen, S. L., & Jackson, S. K. (2006). Agent-based simulation platforms: Review and development recommendations. Simulation, 82(9), 609–623.

    Article  Google Scholar 

  • Robson, S. K., & Traniello, J. F. A. (1999). Key individuals and the organization of labor in ants. Information Processing in Social Insects, 239–259.

  • Smith, G. (2008). Tagging: People-powered metadata for the social web. Berkeley.

  • Surowiecki, J. (2004). The wisdom of crowds: Why the many are smarter than the few and how collective wisdom shapes business, economies, societies and nations. Doubleday.

  • Sumpter, D. J. T. (2006). The principles of collective animal behaviour. Philosophical Transactions of the Royal Society B: Biological Sciences, 361(1465), 5–22.

    Article  Google Scholar 

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Correspondence to Yuan-Chu Hwang.

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Hwang, YC., Yuan, ST. & Weng, JH. A study of the impacts of positive/negative feedback on collective wisdom—case study on social bookmarking sites. Inf Syst Front 13, 265–279 (2011). https://doi.org/10.1007/s10796-009-9186-8

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