  This work aims to evaluate the ability that both probabilistic and
state-of-the-art vector space modeling (VSM) methods provide to well known
machine learning algorithms to identify social network documents to be
classified as aggressive, gender biased or communally charged. To this end, an
exploratory stage was performed first in order to find relevant settings to
test, i.e. by using training and development samples, we trained multiple
algorithms using multiple vector space modeling and probabilistic methods and
discarded the less informative configurations. These systems were submitted to
the competition of the ComMA@ICON'21 Workshop on Multilingual Gender Biased and
Communal Language Identification.
