  Wikipedia has been turned into an immensely popular crowd-sourced
encyclopedia for information dissemination on numerous versatile topics in the
form of subscription free content. It allows anyone to contribute so that the
articles remain comprehensive and updated. For enrichment of content without
compromising standards, the Wikipedia community enumerates a detailed set of
guidelines, which should be followed. Based on these, articles are categorized
into several quality classes by the Wikipedia editors with increasing adherence
to guidelines. This quality assessment task by editors is laborious as well as
demands platform expertise. As a first objective, in this paper, we study
evolution of a Wikipedia article with respect to such quality scales. Our
results show novel non-intuitive patterns emerging from this exploration. As a
second objective we attempt to develop an automated data driven approach for
the detection of the early signals influencing the quality change of articles.
We posit this as a change point detection problem whereby we represent an
article as a time series of consecutive revisions and encode every revision by
a set of intuitive features. Finally, various change point detection algorithms
are used to efficiently and accurately detect the future change points. We also
perform various ablation studies to understand which group of features are most
effective in identifying the change points. To the best of our knowledge, this
is the first work that rigorously explores English Wikipedia article quality
life cycle from the perspective of quality indicators and provides a novel
unsupervised page level approach to detect quality switch, which can help in
automatic content monitoring in Wikipedia thus contributing significantly to
the CSCW community.
