Climate activism has emerged as a powerful force in addressing the urgent challenges posed by climate change. Individuals and organizations passionate about environmental issues use platforms like Twitter to mobilize support, share information, and advocate for policy changes. Unfortunately, amidst the passionate discussions, there has been an unfortunate rise in the prevalence of hate speech on the platform. Some users resort to personal attacks and divisive language, undermining the constructive efforts of climate activists. In this paper, we describe our approaches for three subtasks of ClimateActivism at CASE 2024. For all the three subtasks, we utilize pretrained language models enhanced by ensemble learning. Regarding the second subtask, dedicated to target detection, we experimented with incorporating Named Entity Recognition in the pipeline. Additionally, our models secure the second, third and fifth ranks in the three subtasks respectively.

With the rapid rise of social media platforms, communities have been able to share their passions and interests with the world much more conveniently. This, in turn, has led to individuals being able to spread hateful messages through the use of memes. The classification of such materials requires not only looking at the individual images but also considering the associated text in tandem. Looking at the images or the text separately does not provide the full context. In this paper, we describe our approach to hateful meme classification for the Multimodal Hate Speech Shared Task at CASE 2024. We utilized the same approach in the two subtasks, which involved a classification model based on text and image features obtained using Contrastive Language-Image Pre-training (CLIP) in addition to utilizing BERT-Based models. We then utilize predictions created by both models in an ensemble approach. This approach ranked second in both subtasks, respectively.

Recent advancements in Large Language models (LLMs) have empowered them to achieve text generation capabilities on par with those of humans. These recent advances paired with the wide availability of those models have made Large Language models adaptable in many domains, from scientific writing to story generation along with many others. This recent rise has made it crucial to develop systems to discriminate between human-authored and synthetic text generated by Large Language models (LLMs). Our proposed system for the ALTA shared task, based on ensembling a number of language models, claimed first place on the development set with an accuracy of 99.35% and third place on the test set with an accuracy of 98.35%.

This paper presents the pipeline developed by the AAST-NLP team to address both the persuasion technique detection and disinformation detection shared tasks. The proposed system for all the tasks’ sub-tasks consisted of preprocessing the data and finetuning AraBERT on the given datasets, in addition to several procedures performed for each subtask to adapt to the problems faced in it. The previously described system was used in addition to Dice loss as the loss function for sub-task 1A, which consisted of a binary classification problem. In that sub-task, the system came in eleventh place. We trained the AraBERT for task 1B, which was a multi-label problem with 24 distinct labels, using binary cross-entropy to train a classifier for each label. On that sub-task, the system came in third place. We utilised AraBERT with Dice loss on both subtasks 2A and 2B, ranking second and third among the proposed models for the respective subtasks.