ScalarLab@TRAC2024: Exploring Machine Learning Techniques for Identifying Potential Offline Harm in Multilingual Commentaries

Abstract

The objective of the shared task, Offline Harm Potential Identification (HarmPot-ID), is to build models to predict the offline harm potential of social media texts. "Harm potential" is defined as the ability of an online post or comment to incite offline physical harm such as murder, arson, riot, rape, etc. The first subtask was to predict the level of harm potential, and the second was to identify the group to which this harm was directed towards. This paper details our submissions for the shared task that includes a cascaded SVM model, an XGBoost model, and a TF-IDF weighted Word2Vec embedding-supported SVM model. Our system ranked 4th in the first subtask and 3rd in the second. Several other models that were explored have also been detailed. © 2024 ELRA Language Resource Association.