Z-AGI Labs在ClimateActivism 2024中的研究：社交媒体上的立场与仇恨事件检测
Z-AGI Labs at ClimateActivism 2024: Stance and Hate Event Detection on Social Media
AI总结：
本研究针对社交媒体上气候活动人士遭遇仇恨言论的问题，在CASE 2024共享任务的三个子任务中评估多种经典机器学习模型，得出不同模型适配不同子任务的结论，为相关检测任务的模型选择提供参考。
摘要：
在数字领域，海量数据是洞察社会、政治和经济格局复杂性的关键来源。为满足对事件高质量信息日益增长的需求，以及应对打击仇恨言论的迫切性，本研究推动了CASE 2024气候行动主义立场与仇恨事件检测共享任务的设立。本研究聚焦于在社交媒体上遭遇仇恨言论的气候活动人士，为推特仇恨言论识别研究做出贡献。Z-AGI Labs团队围绕三个子任务——仇恨言论检测（子任务A）、仇恨言论目标识别（子任务B）以及立场检测（子任务C），评估了多种模型，包括基于Tf-Idf的LSTM、Xgboost和LGBM。结果呈现出有趣的差异：Catboost在子任务B（F1值：0.5604）和子任务C（F1值：0.7081）中表现最优，而LGBM是子任务A中性能最佳的模型（F1值：0.8684）。本研究为经典机器学习模型在气候相关仇恨言论与立场检测任务中的适用性提供了宝贵见解，有助于为构建鲁棒机制进行合理的模型选择。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
