@inproceedings{h-c-etal-2024-scalarlab,
title = "{S}calar{L}ab@{TRAC}2024: Exploring Machine Learning Techniques for Identifying Potential Offline Harm in Multilingual Commentaries",
author = "H C, Anagha and
Krishna, Saatvik M. and
Jha, Soumya Sangam and
Rao, Vartika T. and
M, Anand Kumar",
editor = "Kumar, Ritesh and
Ojha, Atul Kr. and
Malmasi, Shervin and
Chakravarthi, Bharathi Raja and
Lahiri, Bornini and
Singh, Siddharth and
Ratan, Shyam",
booktitle = "Proceedings of the Fourth Workshop on Threat, Aggression {\&} Cyberbullying @ LREC-COLING-2024",
month = may,
year = "2024",
address = "Torino, Italia",
publisher = "ELRA and ICCL",
url = "https://aclanthology.org/2024.trac-1.5",
pages = "32--36",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T ScalarLab@TRAC2024: Exploring Machine Learning Techniques for Identifying Potential Offline Harm in Multilingual Commentaries
%A H C, Anagha
%A Krishna, Saatvik M.
%A Jha, Soumya Sangam
%A Rao, Vartika T.
%A M, Anand Kumar
%Y Kumar, Ritesh
%Y Ojha, Atul Kr.
%Y Malmasi, Shervin
%Y Chakravarthi, Bharathi Raja
%Y Lahiri, Bornini
%Y Singh, Siddharth
%Y Ratan, Shyam
%S Proceedings of the Fourth Workshop on Threat, Aggression & Cyberbullying @ LREC-COLING-2024
%D 2024
%8 May
%I ELRA and ICCL
%C Torino, Italia
%F h-c-etal-2024-scalarlab
%X 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.
%U https://aclanthology.org/2024.trac-1.5
%P 32-36
Markdown (Informal)
[ScalarLab@TRAC2024: Exploring Machine Learning Techniques for Identifying Potential Offline Harm in Multilingual Commentaries](https://aclanthology.org/2024.trac-1.5) (H C et al., TRAC-WS 2024)
ACL