  Social Networking Sites (SNS) are one of the most important ways of
communication. In particular, microblogging sites are being used as analysis
avenues due to their peculiarities (promptness, short texts...). There are
countless researches that use SNS in novel manners, but machine learning has
focused mainly in classification performance rather than interpretability
and/or other goodness metrics. Thus, state-of-the-art models are black boxes
that should not be used to solve problems that may have a social impact. When
the problem requires transparency, it is necessary to build interpretable
pipelines. Although the classifier may be interpretable, resulting models are
too complex to be considered comprehensible, making it impossible for humans to
understand the actual decisions. This paper presents a feature selection
mechanism that is able to improve comprehensibility by using less but more
meaningful features while achieving good performance in microblogging contexts
where interpretability is mandatory. Moreover, we present a ranking method to
evaluate features in terms of statistical relevance and bias. We conducted
exhaustive tests with five different datasets in order to evaluate
classification performance, generalisation capacity and complexity of the
model. Results show that our proposal is better and the most stable one in
terms of accuracy, generalisation and comprehensibility.
