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

Authored by Yuan-Chu Hwang, Soe-Tsyr Yuan, Jung-Hui Weng

Date Published: 2011-04

DOI: 10.1007/s10796-009-9186-8

Sponsors: No sponsors listed

Platforms: No platforms listed

Model Documentation: Other Narrative

Model Code URLs: Model code not found

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.

Tags

Agent-Based Modeling and Simulation Collective wisdom Positive/negative impact Social bookmarking site