  This study delves into the pivotal role played by non-experts in knowledge
production on open collaboration platforms, with a particular focus on the
intricate process of tag development that culminates in the proposal of new
glitch classes. Leveraging the power of Association Rule Mining (ARM), this
research endeavors to unravel the underlying dynamics of collaboration among
citizen scientists. By meticulously quantifying tag associations and
scrutinizing their temporal dynamics, the study provides a comprehensive and
nuanced understanding of how non-experts collaborate to generate valuable
scientific insights. Furthermore, this investigation extends its purview to
examine the phenomenon of ideological convergence within online citizen science
knowledge production. To accomplish this, a novel measurement algorithm, based
on the Mann-Kendall Trend Test, is introduced. This innovative approach sheds
illuminating light on the dynamics of collaborative knowledge production,
revealing both the vast opportunities and daunting challenges inherent in
leveraging non-expert contributions for scientific research endeavors. Notably,
the study uncovers a robust pattern of convergence in ideology, employing both
the newly proposed convergence testing method and the traditional approach
based on the stationarity of time series data. This groundbreaking discovery
holds significant implications for understanding the dynamics of online citizen
science communities and underscores the crucial role played by non-experts in
shaping the scientific landscape of the digital age. Ultimately, this study
contributes significantly to our understanding of online citizen science
communities, highlighting their potential to harness collective intelligence
for tackling complex scientific tasks and enriching our comprehension of
collaborative knowledge production processes in the digital age.
