  The widespread of offensive content online has become a reason for great
concern in recent years, motivating researchers to develop robust systems
capable of identifying such content automatically. With the goal of carrying
out a fair evaluation of these systems, several international competitions have
been organized, providing the community with important benchmark data and
evaluation methods for various languages. Organized since 2019, the HASOC (Hate
Speech and Offensive Content Identification) shared task is one of these
initiatives. In its fourth iteration, HASOC 2022 included three subtracks for
English, Hindi, and Marathi. In this paper, we report the results of the HASOC
2022 Marathi subtrack which provided participants with a dataset containing
data from Twitter manually annotated using the popular OLID taxonomy. The
Marathi track featured three additional subtracks, each corresponding to one
level of the taxonomy: Task A - offensive content identification (offensive vs.
non-offensive); Task B - categorization of offensive types (targeted vs.
untargeted), and Task C - offensive target identification (individual vs. group
vs. others). Overall, 59 runs were submitted by 10 teams. The best systems
obtained an F1 of 0.9745 for Subtrack 3A, an F1 of 0.9207 for Subtrack 3B, and
F1 of 0.9607 for Subtrack 3C. The best performing algorithms were a mixture of
traditional and deep learning approaches.
