AAST - NLP at C limate A ctivism 2024: Ensemble-Based Climate Activism Stance and Hate Speech Detection : Leveraging Pretrained Language Models (2024)

Abstract: 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.

## introduction climate activism has emerged as a formidable force in contemporary society, reflecting a collective global consciousness towards environmental stewardship. the advocates of climate activism ardently emphasize the urgency of addressing climate change as a paramount global challenge. through various channels, such as organized protests, advocacy campaigns, and international collaborations, climate activists strive to raise awareness about the detrimental impact of human activities on the planet's ecosystems @xcite . social media has played a pivotal role in amplifying the voices of climate activists, providing a powerful platform for the dissemination of information and the mobilization of global communities. platforms like twitter, instagram, and facebook have facilitated the rapid spread of awareness campaigns, enabling activists to reach diverse audiences and gar-ner widespread support for climate action. @xcite however, the same social media channels have also been susceptible to the spread of misinformation and targeted attacks against climate activists @xcite . instances of hate speech and online harassment have, unfortunately, been prevalent, underscoring the double-edged nature of social media in the context of climate activism. the climate activism 2024 shared task @xcite delves into this significant subject by providing a dataset that encourages collaboration among researchers to address this crucial issue. the paper is organized into several key sections: related work, dataset and task description, methodology, results, and a discussion leading to a conclusion. ## related work in the realm of social media, the challenge of hate speech detection arises as a pressing concern (jahan and oussalah, 2023b). a number of researcher have proposed models to tackle this issue. language models, in particular, have been a major driving force or this recent succes. roberta, for instance, was used in detecting hate speech from social media data @xcite . some bert based models were trained specifically for hate speech detection and achieved incredible results @xcite . language models were also adapted to multiple languages and were noticed to perform high results @xcite . a number of papers provide a comprehensive overview over the latest challenges and trend in hate speech detection, some of which serve as a starting point for any researcher working on this topic @xcite . hate speech manifests in various forms, and scholars have focused on creating systems to tackle issues like cyber bullying @xcite , racism @xcite , and sexism @xcite . despite the ongoing and comprehensive endeavors of researchers, as far as we are aware, there has not been a unified research initiative to monitor hate speech specifically directed at climate activists, a significant and alarming occurrence. ## dataset & task the shared task on climate activism stance and hate event detection at case 2024 1 consists of three main subtasks.each subtask will be discussed in details in the following subsections. the provided dataset primarily comprises tweets expressing either support or opposition towards climate activists in various contexts @xcite . the subsequent subsections will present an overview of the distribution for each dataset, emphasizing the challenges posed by imbalances, particularly instances where certain classes were underrepresented. ## methodology in the following subsections, we will expand on the proposed models for each subtask. we will also expand on the main ideas we experimented on to tackle the class imbalance issue we encountered. ## results this section elaborates on the results obtained from using the mentioned systems. it's crucial to note that roberta, xlm-roberta, and hatebert underwent multiple training sessions with varying dataset distributions through resampling. additionally, both base and large versions were experimented with for roberta and xlm-roberta. the top-k ensemble method selected the highest k submissions for ensembling. ## discussion & future work the results obtained show that leveraging pretrained models for the classification of hate tweets could provide very promising results, even when faced with unbalanced data. these results form a great basis for further research, including but not limited to incorporating more language models into the ensemble, such as the falcon series of models @xcite or mistral @xcite . creating synthetic data with the aim of enhancing model robustness or improving performance on underrepresented classes or ones the model faces difficulties in identifying is also an intriguing strategy. attempting different hyperparameter configurations is also worthy of further investigation. overall, with further refinement, this approach could definitely have a real impact on reducing the hate experienced by climate activists all around the world. ## conclusion this study centers on analyzing tweets that convey opinions and emotions, but regrettably, these tweets are also employed as channels for disseminating hate speech, propaganda, and extremist ideologies. particularly, amidst the recent surge in climate activism, social media emerged as a primary platform not just for raising awareness but unfortunately for spreading negativity as well. the increasing prevalence of offensive content on social media presents challenges in efficiently identifying and moderating such material. to tackle this alarming issue, we present our solution based on ensembling top-k performing models. language models remain the crucial tool for addressing contemporary natural language processing (nlp) challenges, consistently attaining top positions across various subtasks. our research findings paves the way for upcoming enhancements to address and mitigate this highly concerning issue in the near future.