@inproceedings{narayan-biswal-2024-z,
title = "{Z}-{AGI} Labs at {C}limate{A}ctivism 2024: Stance and Hate Event Detection on Social Media",
author = "Narayan, Nikhil and
Biswal, Mrutyunjay",
editor = {H{\"u}rriyeto{\u{g}}lu, Ali and
Tanev, Hristo and
Thapa, Surendrabikram and
Uludo{\u{g}}an, G{\"o}k{\c{c}}e},
booktitle = "Proceedings of the 7th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE 2024)",
month = mar,
year = "2024",
address = "St. Julians, Malta",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.case-1.22",
pages = "161--165",
abstract = "In the digital realm, rich data serves as a crucial source of insights into the complexities of social, political, and economic landscapes. Addressing the growing need for high-quality information on events and the imperative to combat hate speech, this research led to the establishment of the Shared Task on Climate Activism Stance and Hate Event Detection at CASE 2024. Focused on climate activists contending with hate speech on social media, our study contributes to hate speech identification from tweets. Analyzing three sub-tasks - Hate Speech Detection (Sub-task A), Targets of Hate Speech Identification (Sub-task B), and Stance Detection (Sub-task C) - Team Z-AGI Labs evaluated various models, including LSTM, Xgboost, and LGBM based on Tf-Idf. Results unveiled intriguing variations, with Catboost excelling in Subtask-B (F1: 0.5604) and Subtask-C (F1: 0.7081), while LGBM emerged as the top-performing model for Subtask-A (F1: 0.8684). This research provides valuable insights into the suitability of classical machine learning models for climate hate speech and stance detection, aiding informed model selection for robust mechanisms.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="narayan-biswal-2024-z">
<titleInfo>
<title>Z-AGI Labs at ClimateActivism 2024: Stance and Hate Event Detection on Social Media</title>
</titleInfo>
<name type="personal">
<namePart type="given">Nikhil</namePart>
<namePart type="family">Narayan</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Mrutyunjay</namePart>
<namePart type="family">Biswal</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2024-03</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 7th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE 2024)</title>
</titleInfo>
<name type="personal">
<namePart type="given">Ali</namePart>
<namePart type="family">Hürriyetoğlu</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Hristo</namePart>
<namePart type="family">Tanev</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Surendrabikram</namePart>
<namePart type="family">Thapa</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Gökçe</namePart>
<namePart type="family">Uludoğan</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">St. Julians, Malta</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>In the digital realm, rich data serves as a crucial source of insights into the complexities of social, political, and economic landscapes. Addressing the growing need for high-quality information on events and the imperative to combat hate speech, this research led to the establishment of the Shared Task on Climate Activism Stance and Hate Event Detection at CASE 2024. Focused on climate activists contending with hate speech on social media, our study contributes to hate speech identification from tweets. Analyzing three sub-tasks - Hate Speech Detection (Sub-task A), Targets of Hate Speech Identification (Sub-task B), and Stance Detection (Sub-task C) - Team Z-AGI Labs evaluated various models, including LSTM, Xgboost, and LGBM based on Tf-Idf. Results unveiled intriguing variations, with Catboost excelling in Subtask-B (F1: 0.5604) and Subtask-C (F1: 0.7081), while LGBM emerged as the top-performing model for Subtask-A (F1: 0.8684). This research provides valuable insights into the suitability of classical machine learning models for climate hate speech and stance detection, aiding informed model selection for robust mechanisms.</abstract>
<identifier type="citekey">narayan-biswal-2024-z</identifier>
<location>
<url>https://aclanthology.org/2024.case-1.22</url>
</location>
<part>
<date>2024-03</date>
<extent unit="page">
<start>161</start>
<end>165</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Z-AGI Labs at ClimateActivism 2024: Stance and Hate Event Detection on Social Media
%A Narayan, Nikhil
%A Biswal, Mrutyunjay
%Y Hürriyetoğlu, Ali
%Y Tanev, Hristo
%Y Thapa, Surendrabikram
%Y Uludoğan, Gökçe
%S Proceedings of the 7th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE 2024)
%D 2024
%8 March
%I Association for Computational Linguistics
%C St. Julians, Malta
%F narayan-biswal-2024-z
%X In the digital realm, rich data serves as a crucial source of insights into the complexities of social, political, and economic landscapes. Addressing the growing need for high-quality information on events and the imperative to combat hate speech, this research led to the establishment of the Shared Task on Climate Activism Stance and Hate Event Detection at CASE 2024. Focused on climate activists contending with hate speech on social media, our study contributes to hate speech identification from tweets. Analyzing three sub-tasks - Hate Speech Detection (Sub-task A), Targets of Hate Speech Identification (Sub-task B), and Stance Detection (Sub-task C) - Team Z-AGI Labs evaluated various models, including LSTM, Xgboost, and LGBM based on Tf-Idf. Results unveiled intriguing variations, with Catboost excelling in Subtask-B (F1: 0.5604) and Subtask-C (F1: 0.7081), while LGBM emerged as the top-performing model for Subtask-A (F1: 0.8684). This research provides valuable insights into the suitability of classical machine learning models for climate hate speech and stance detection, aiding informed model selection for robust mechanisms.
%U https://aclanthology.org/2024.case-1.22
%P 161-165
Markdown (Informal)
[Z-AGI Labs at ClimateActivism 2024: Stance and Hate Event Detection on Social Media](https://aclanthology.org/2024.case-1.22) (Narayan & Biswal, CASE-WS 2024)
ACL