Difference between revisions of "Modeling of Decline Dynamics of Knowledge Sharing Networks (Ksnets) - a Wikipedia Case"
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| − | '''Modeling of Decline Dynamics of Knowledge Sharing Networks (Ksnets) - a Wikipedia Case''' - scientific work related to Wikipedia quality published in 2018, written by Rong-Huei Chen, Shi-Chung Chang and Peter B. Luh. | + | '''Modeling of Decline Dynamics of Knowledge Sharing Networks (Ksnets) - a Wikipedia Case''' - scientific work related to [[Wikipedia quality]] published in 2018, written by [[Rong-Huei Chen]], [[Shi-Chung Chang]] and [[Peter B. Luh]]. |
== Overview == | == Overview == | ||
| − | Online knowledge sharing networks (KSNets) have made significant impacts on the economy as well as wellbeing of societies through sharing. One of the most successful KSNets is Wikipedia that allows users to create contents in a collaborative manner and to provide fast and easy access at no cost to users. Recent research, however, has shown that the numbers of | + | Online knowledge sharing networks (KSNets) have made significant impacts on the economy as well as wellbeing of societies through sharing. One of the most successful KSNets is [[Wikipedia]] that allows users to create contents in a collaborative manner and to provide fast and easy access at no cost to users. Recent research, however, has shown that the numbers of “[[Wikipedians]]” and new page creations have been declining, reflecting decrease in user contributions and in new contents. To facilitate management for sustainability, this paper aims at quantitatively modeling how the decline in new contents affects the number of Wikipedians and in turn content creations, and predicting decline start time and speed based on available Wikipedia data. The novel modeling approach adopts auto-regression with an extended Bass Diffusion model (AREBDM) embedded to describe the Wikipedia-wide evolutions of the number of Wikipedians and content developments. Model parameters are then extracted by a nonlinear least square method from early Wikipedia data. Simulation predictions match well with actual Wikipedia decline trajectories of later stages. Authors analysis shows that the decline of new page creation leads in time the decline of the number of new Wikipedians, and the decline speed increases with the decrease of new contents. Authors approach therefore has the potential to predict decline time and speed so that proactive actions can be taken as early as possible. |
Revision as of 00:18, 4 September 2019
Modeling of Decline Dynamics of Knowledge Sharing Networks (Ksnets) - a Wikipedia Case - scientific work related to Wikipedia quality published in 2018, written by Rong-Huei Chen, Shi-Chung Chang and Peter B. Luh.
Overview
Online knowledge sharing networks (KSNets) have made significant impacts on the economy as well as wellbeing of societies through sharing. One of the most successful KSNets is Wikipedia that allows users to create contents in a collaborative manner and to provide fast and easy access at no cost to users. Recent research, however, has shown that the numbers of “Wikipedians” and new page creations have been declining, reflecting decrease in user contributions and in new contents. To facilitate management for sustainability, this paper aims at quantitatively modeling how the decline in new contents affects the number of Wikipedians and in turn content creations, and predicting decline start time and speed based on available Wikipedia data. The novel modeling approach adopts auto-regression with an extended Bass Diffusion model (AREBDM) embedded to describe the Wikipedia-wide evolutions of the number of Wikipedians and content developments. Model parameters are then extracted by a nonlinear least square method from early Wikipedia data. Simulation predictions match well with actual Wikipedia decline trajectories of later stages. Authors analysis shows that the decline of new page creation leads in time the decline of the number of new Wikipedians, and the decline speed increases with the decrease of new contents. Authors approach therefore has the potential to predict decline time and speed so that proactive actions can be taken as early as possible.
