and a random sample of 1,000 articles from the 2006/11/30
copy of the Wikipedia database. We used these data to generate a time series of monthly data points for
the number of article edits, the number of article editors, and article length. Several studies have
suggested that there may be a connection between the number of edits and the quality of Wikipedia
articles (e.g., [21, 24]). One needs to remember, however, that although the number of edits can often
serve as an indirect indicator of high quality, for controversial articles a high number of edits can also
mean edit wars and vandalism. In addition, we looked at the logs of Featured Article Review (FAR) and
Featured Article Removal Candidate3
(FARC) process logs for the FFAs.
The research method used in this study consisted of a combination of (1) a conceptual modeling of
Wikipedia information processes, (2) a grounded analysis of quality evaluation discussions, and (3) an
analysis of time series data on article attributes in Wikipedia.
Activity theory provided us with a conceptual framework for reasoning systematically about the general
context of IQ in Wikipedia: the hierarchical nature of goal-oriented activities, the integration points of
different sociocultural aspects of the activity system, and the roles and dynamics of the quality and quality
assurance infrastructure of Wikipedia articles as a whole.
The logs of quality discussions and votes provided valuable insight into context-specific quality assurance
decision-making processes. The time series analysis of article attributes (the number of edits, the number
of editors, and article length), on the other hand, helped to identify structural trends and patterns in the
articles’ IQ. Graphical data analysis techniques such as run sequence and autocorrelation plots were used
to test the data for non-randomness and identify the trends [2, 3]. The results of the structural analysis
informed and guided our analysis of local context-specific quality assurance practices and decisionmaking
processes, and allowed us to identify sources of quality variance.