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In this paper, we discuss the development of a multilingual dataset annotatedwith a hierarchical, fine-grained tagset marking different types of aggressionand the "context" in which they occur. The context, here, is defined by theconversational thread in which a specific comment occurs and also the "type" ofdiscursive role that the comment is performing with respect to the previouscomment. The initial dataset, being discussed here (and made available as partof the ComMA@ICON shared task), consists of a total 15,000 annotated commentsin four languages - Meitei, Bangla, Hindi, and Indian English - collected fromvarious social media platforms such as YouTube, Facebook, Twitter and Telegram.As is usual on social media websites, a large number of these comments aremultilingual, mostly code-mixed with English. The paper gives a detaileddescription of the tagset being used for annotation and also the process ofdeveloping a multi-label, fine-grained tagset that can be used for markingcomments with aggression and bias of various kinds including gender bias,religious intolerance (called communal bias in the tagset), class/caste biasand ethnic/racial bias. We also define and discuss the tags that have been usedfor marking different the discursive role being performed through the comments,such as attack, defend, etc. We also present a statistical analysis of thedataset as well as results of our baseline experiments with developing anautomatic aggression identification system using the dataset developed.

The ComMA Dataset V0.2: Annotating Aggression and Bias in Multilingual Social Media Discourse