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

LUC at ComMA-2021 Shared Task: Multilingual Gender Biased and Communal Language Identification without using linguistic features