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.

通过分析社交媒体上的推文，研究以气候活动家对抗仇恨言论为中心，利用经典机器学习模型对气候仇恨言论和立场进行识别和检测，以提供关于模型选择的有价值见解。

Z-AGI 实验室参与社交媒体上的气候行动 2024：立场和仇恨事件检测

Z-AGI Labs at ClimateActivism 2024: Stance and Hate Event Detection on  Social Media

The task of identifying public opinions on social media, particularly
regarding climate activism and the detection of hate events, has emerged as a
critical area of research in our rapidly changing world. With a growing number
of people voicing either to support or oppose to climate-related issues -
understanding these diverse viewpoints has become increasingly vital. Our team,
MasonPerplexity, participates in a significant research initiative focused on
this subject. We extensively test various models and methods, discovering that
our most effective results are achieved through ensemble modeling, enhanced by
data augmentation techniques like back-translation. In the specific components
of this research task, our team achieved notable positions, ranking 5th, 1st,
and 6th in the respective sub-tasks, thereby illustrating the effectiveness of
our approach in this important field of study.

社会媒体上识别公众舆论，尤其是关于气候行动和仇恨事件检测，已成为研究的一个关键领域。我们团队通过广泛测试各种模型和方法，发现我们最有效的结果是通过集成建模并结合后翻译等数据增强技术实现的。在此研究任务的具体组成部分中，我们团队在相应的子任务中分别排名第 5、第 1 和第 6，从而说明了我们在这一重要研究领域中的方法的有效性。

2024 年气候行动中的 MasonPerplexity: 集成先进的集合技术和数据增强技术用于气候行动立场和仇恨事件识别

MasonPerplexity at ClimateActivism 2024: Integrating Advanced Ensemble  Techniques and Data Augmentation for Climate Activism Stance and Hate Event  Identification