ScalarLab@TRAC2024: Exploring Machine Learning Techniques for Identifying Potential Offline Harm in Multilingual Commentaries
Anagha H C, Saatvik M. Krishna, Soumya Sangam Jha, Vartika T. Rao, Anand Kumar M
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. Several other models that were explored have also been detailed.- Anthology ID:
- 2024.trac-1.5
- Volume:
- Proceedings of the Fourth Workshop on Threat, Aggression & Cyberbullying @ LREC-COLING-2024
- Month:
- May
- Year:
- 2024
- Address:
- Torino, Italia
- Editors:
- Ritesh Kumar, Atul Kr. Ojha, Shervin Malmasi, Bharathi Raja Chakravarthi, Bornini Lahiri, Siddharth Singh, Shyam Ratan
- Venues:
- TRAC | WS
- SIG:
- Publisher:
- ELRA and ICCL
- Note:
- Pages:
- 32–36
- Language:
- URL:
- https://aclanthology.org/2024.trac-1.5
- DOI:
- Cite (ACL):
- Anagha H C, Saatvik M. Krishna, Soumya Sangam Jha, Vartika T. Rao, and Anand Kumar M. 2024. ScalarLab@TRAC2024: Exploring Machine Learning Techniques for Identifying Potential Offline Harm in Multilingual Commentaries. In Proceedings of the Fourth Workshop on Threat, Aggression & Cyberbullying @ LREC-COLING-2024, pages 32–36, Torino, Italia. ELRA and ICCL.
- Cite (Informal):
- ScalarLab@TRAC2024: Exploring Machine Learning Techniques for Identifying Potential Offline Harm in Multilingual Commentaries (H C et al., TRAC-WS 2024)
- PDF:
- https://preview.aclanthology.org/add_acl24_videos/2024.trac-1.5.pdf