Order and disorder in the evolution of online knowledge community: an investigation of the chaotic behavior in social tagging systems with evidence of stack overflow
| Date | 02 January 2023 |
| Pages | 132-152 |
| DOI | https://doi.org/10.1108/AJIM-08-2022-0353 |
| Published date | 02 January 2023 |
| Subject Matter | Library & information science,Information behaviour & retrieval,Information & knowledge management,Information management & governance,Information management |
| Author | Yanqing Shi,Hongye Cao,Si Chen |
Order and disorder in the evolution
of online knowledge community:
an investigation of the chaotic
behavior in social tagging systems
with evidence of stack overflow
Yanqing Shi
College of Information Management, Nanjing Agricultural University,
Nanjing, China
Hongye Cao
Northwestern Polytechnical University, Xi’an, China, and
Si Chen
Nanjing University of Information Science and Technology, Nanjing, China
Abstract
Purpose –Online question-and-answer (Q&A) communities serve as important channels for knowledge
diffusion. The purpose of this study is to investigate the dynamic development process of online knowledge
systems and explore the final or progressive state of system development. By measuring the nonlinear
characteristics of knowledge systems from the perspective of complexity science, the authors aimto enrich the
perspective and method of the research on the dynamics of knowledge systems, and to deeply understand the
behavior rules of knowledge systems.
Design/methodology/approach –The authors collected data from the programming-related Q&A site
Stack Overflow for a ten-year period (2008–2017) and included 48,373 tags in the analyses. The number of tags
is taken as the time series, the correlation dimension and the maximum Lyapunov index are used to examine
the chaos of the system and the Volterra series multistep forecast method is used to predict the system state.
Findings –There are strange attractors in the system, the whole system is complex but bounded and its
evolution is bound to approach a relatively stable range. Empirical analyses indicate that chaos exists in the
process of knowledge sharing in this social labeling system, and the period of change over time is about
one week.
Originality/value –This study contributes to revealing the evolutionary cycle of knowledge stock in online
knowledge systems and further indicates how this dynamic evolution can help in the setting of platform
mechanics and resource inputs.
Keywords Online forum, Q&A community, Nonlinear system, Chaotic behavior
Paper type Research paper
1. Introduction
Knowledge sharing is fundamentally the diffusion of knowledge among different types of
users (Al-Emran et al., 2018). Knowledge diffusion among a network of users is a complex
approach that exhibits strong synergism among such factors as knowledge providers,
knowledge receivers, the internal environment of knowledge network constructs and the
external knowledge and technical environment (Abramo et al., 2020). Seen as a network, those
factors interact intimately. Knowledge diffusion, influenced as it is by both internal nonlinear
factors and external random factors, is characterized by a mixture of certainty and
uncertainty (Todo et al., 2016). This unique characteristic, which gives rise to randomness, is
AJIM
76,1
132
This work was supported by the National Social Science Foundation of China (21CTQ024).
The current issue and full text archive of this journal is available on Emerald Insight at:
https://www.emerald.com/insight/2050-3806.htm
Received 4 August 2022
Revised 29 September 2022
7 November 2022
Accepted 19 November 2022
Aslib Journal of Information
Management
Vol. 76 No. 1, 2024
pp. 132-152
© Emerald Publishing Limited
2050-3806
DOI 10.1108/AJIM-08-2022-0353
derived from the sensitivity of systems to initial parameters (Giaer, 2002). To investigate the
gradual and final process of knowledge system evolution, this paper has analyzed and
validated the chaotic dynamics of these systems.
Prior literature demonstrates that there are clusters (Ferrara, 2012;Girvan and Newman,
2002) in a huge variety of real-world networks, be they biological (Ravasz et al., 2002),
economic (Gui et al., 2014;Uzzi et al., 2007) or otherwise. In the evolution of a knowledge
system, knowledge cannot be produced and developed in isolation from other knowledge
points; on the contrary, it is certain that different types of knowledge display strong or weak
relevance to one another (Norstr€
om et al., 2020). Kho et al. (2013) find that the relations of
newly entering keywords display a high degree of clustering in publications related to
management of technology. Based on the analysis of knowledge cluster development in
citation networks, Kajikawa et al. (2008) investigate the emerging research topics and growth
trends in the energy domain. Therefore, these studies show that the development of
knowledge can aptly be considered as the growth and evolution of knowledge clusters to
some extent. It is meaningful to investigate knowledge relations and knowledge sharing
mechanisms according to the development of such clusters.
From the perspective of complexity science, an online question-and-answer (Q&A)
community is a dynamic and open system. And, an open system with matter and energy
exchange with the environment would exhibit the characteristics of dissipative structure
under nonlinear action (Willems, 1972). In general, chaos is prevalent in dissipative systems.
A chaotic system may appear random from the outside as does a disordered system, but the
difference is the ability for that system to organize itself into an ordered state. Concerned with
the features of the online knowledge sharing communities, the knowledge system is complex
and seemingly disordered in appearance while it is ordered implicitly. This paper analyzes
the rules embedded with the chaos theory.
Prior research on chaos has developed a systematic model based mainly on time-series
observations (Khalil et al., 2006;Sugihara and May, 1990). Along with the development of
complex network research, knowledge network studies, in borrowing ideas from social
network research methods, have broadened their horizons to include networks structured
around keywords and tags. Moreover, with the rapid growth of social networks, users are
more active and engaged participants in communication within knowledge networks (Chai
and Kim, 2012). Under these conditions, tagging systems, which are built based on group
cognition and labeling, have emerged as a hot research topic (Surowiecki, 2010;Yang et al.,
2021). In this paper, we used question tags on the social Q&A community Stack Overflow to
represent points of knowledge and investigated the characteristics and pattern of the tags’
evolution over a period of time. Specifically, the existence of chaos is established by
calculating two characteristic quantities, fractal dimension and Lyapunov exponent, which
describe the chaos of the system. This paper represents the application of chaos-theoretic
principles to a new or underexplored area of study and also enriches the perspective and
methods of research on the dynamics of online knowledge systems.
2. Literature review
2.1 Quantitative chaotic recognition methods
Prerequisite to chaotic time-series analysis is chaos identification (Alemu, 2018). The
quantitative analysis of chaotic characteristics is mainly conducted through elucidating the
critical index of the chaotic system, including Lyapunov exponents, Kolmogorov entropy and
correlation dimension (Nerenberg and Essex, 1990;Wolf et al., 1985;Benettin et al., 1976;Lai
and Lerner, 1998).
Chaotic dynamics is mainly concerned with orbit revisit problems. Lyapunov exponents
are the exponential rates that represent the divergence or convergence of nearby orbits in
Social tagging
systems
133
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