  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.
