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

TL;DR

This paper presents MasonPerplexity's approach to identifying climate activism opinions and hate events on social media, leveraging ensemble models and data augmentation to improve accuracy and achieve top rankings.

Contribution

The study introduces an ensemble modeling framework combined with data augmentation techniques for stance and hate event detection in climate activism social media data.

Findings

Achieved top rankings in sub-tasks, including 1st place.

Demonstrated effectiveness of ensemble models with data augmentation.

Validated approach improves detection accuracy in social media analysis.

Abstract

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.