  Wikipedia is the largest online encyclopedia, used by algorithms and web
users as a central hub of reliable information on the web. The quality and
reliability of Wikipedia content is maintained by a community of volunteer
editors. Machine learning and information retrieval algorithms could help scale
up editors' manual efforts around Wikipedia content reliability. However, there
is a lack of large-scale data to support the development of such research. To
fill this gap, in this paper, we propose Wiki-Reliability, the first dataset of
English Wikipedia articles annotated with a wide set of content reliability
issues. To build this dataset, we rely on Wikipedia "templates". Templates are
tags used by expert Wikipedia editors to indicate content issues, such as the
presence of "non-neutral point of view" or "contradictory articles", and serve
as a strong signal for detecting reliability issues in a revision. We select
the 10 most popular reliability-related templates on Wikipedia, and propose an
effective method to label almost 1M samples of Wikipedia article revisions as
positive or negative with respect to each template. Each positive/negative
example in the dataset comes with the full article text and 20 features from
the revision's metadata. We provide an overview of the possible downstream
tasks enabled by such data, and show that Wiki-Reliability can be used to train
large-scale models for content reliability prediction. We release all data and
code for public use.
