  Nowadays, thanks to Web 2.0 technologies, people have the possibility to
generate and spread contents on different social media in a very easy way. In
this context, the evaluation of the quality of the information that is
available online is becoming more and more a crucial issue. In fact, a constant
flow of contents is generated every day by often unknown sources, which are not
certified by traditional authoritative entities. This requires the development
of appropriate methodologies that can evaluate in a systematic way these
contents, based on `objective' aspects connected with them. This would help
individuals, who nowadays tend to increasingly form their opinions based on
what they read online and on social media, to come into contact with
information that is actually useful and verified. Wikipedia is nowadays one of
the biggest online resources on which users rely as a source of information.
The amount of collaboratively generated content that is sent to the online
encyclopedia every day can let to the possible creation of low-quality articles
(and, consequently, misinformation) if not properly monitored and revised. For
this reason, in this paper, the problem of automatically assessing the quality
of Wikipedia articles is considered. In particular, the focus is on the
analysis of hand-crafted features that can be employed by supervised machine
learning techniques to perform the classification of Wikipedia articles on
qualitative bases. With respect to prior literature, a wider set of
characteristics connected to Wikipedia articles are taken into account and
illustrated in detail. Evaluations are performed by considering a labeled
dataset provided in a prior work, and different supervised machine learning
algorithms, which produced encouraging results with respect to the considered
features.
