The automatic identification of hate speech constitutes an important task, playing a relevant role towards inclusivity. In these terms, the shared task on Climate Activism Stance and Hate Event Detection at CASE 2024 proposes the analysis of Twitter messages related to climate change activism for three subtasks. Subtasks A and C aim at detecting hate speech and establishing the stance of the tweet, respectively, while subtask B seeks to determine the target of the hate speech. In this paper, we describe our approach to the given subtasks. Our systems leverage transformer-based multi-task learning. Additionally, since the dataset contains a low number of tweets, we have studied the effect of adding external data to increase the learning of the model. With our approach we achieve the fourth position on subtask C on the final leaderboard, with minimal difference from the first position, showcasing the strength of multi-task learning.

CASE @ EACL 2024 proposes a shared task on Stance and Hate Event Detection for Climate Activism discourse. For our participation in the stance detection task, we propose an ensemble of different approaches: a transformer-based model (RoBERTa), a generative Large Language Model (Llama 2), and a Multi-Task Learning model. Our main goal is twofold: to study the effect of augmenting the training data with external datasets, and to examine the contribution of several, diverse models through a voting ensemble. The results show that if we take the best configuration during training for each of the three models (RoBERTa, Llama 2 and MTL), the ensemble would have ranked first with the highest F1 on the leaderboard for the stance detection subtask.