Z-AGI Labs at ClimateActivism 2024: Stance and Hate Event Detection on Social Media
This work addresses the need for effective tools to combat hate speech and analyze stances for climate activists on social media, but it is incremental as it applies existing methods to a new dataset.
The research tackled the problem of detecting hate speech and stance in social media tweets related to climate activism, evaluating models like LSTM, Xgboost, and LGBM, with results showing LGBM achieving an F1 score of 0.8684 for hate speech detection and Catboost scoring 0.7081 for stance detection.
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.