Predicting the type and target of offensive social media posts in Marathi


The presence of offensive language on social media is very common motivating platforms to invest in strategies to make communities safer. This includes developing robust machine learning systems capable of recognizing offensive content online. Apart from a few notable exceptions, most research on automatic offensive language identification has dealt with English and a few other high-resource languages such as French, German, and Spanish. In this paper, we address this gap by tackling offensive language identification in Marathi, a low-resource Indo-Aryan language spoken in India. We introduce the Marathi Offensive Language Dataset v.2.0 or MOLD 2.0 and present multiple experiments on this dataset. MOLD 2.0 is a much larger version of MOLD with expanded annotation to the levels B (type) and C (target) of the popular OLID taxonomy. MOLD 2.0 is the first hierarchical offensive language dataset compiled for Marathi, thus opening new avenues for research in low-resource Indo-Aryan languages. Finally, we also introduce SeMOLD, a larger dataset annotated following the semi-supervised methods presented in SOLID (Rosenthal et al. in SOLID: a large-scale semi-supervised dataset for offensive language identification. In: Findings of ACL, 2021).

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Additional Information: Copyright © Springer Nature B.V. 2022. This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use [], but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org10.1007s13278-022-00906-8
Uncontrolled Keywords: Offensive language identification,Hate speech,Machine learning,Deep learning,Low-language resources
Last Modified: 24 Apr 2024 17:51
Date Deposited: 02 Feb 2023 13:36
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Related URLs: https://link.sp ... 278-022-00906-8 (Publisher URL)
http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Article
Published Date: 2022-12
Published Online Date: 2022-07-09
Accepted Date: 2022-06-10
Authors: Zampieri, Marcos
Ranasinghe, Tharindu (ORCID Profile 0000-0003-3207-3821)
Chaudhari, Mrinal
Gaikwad, Saurabh
Krishna, Prajwal
Nene, Mayuresh
Paygude, Shrunali



Version: Accepted Version

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