Malta

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.

EACL 2024

HAMiSoN-Ensemble at ClimateActivism 2024: Ensemble of RoBERTa, Llama 2, and Multi-task for Stance Detection

llama 2

ensemble

stance detection

multi-task learning

Welcome to the 18th Conference of the European Chapter of the Association for Computational Linguistics (EACL). Continuing its mission of expanding and involving the science community of all European countries, EACL has selected the Malta community for the 18th EACL. Considering the importance of physical interaction among researchers, the conference will be held at the Hotel Radisson Blu, St. Julians, in Malta, from 17 to 22 of March, 2024. The conference will also feature the possibility to participate virtually. As the flagship European conference in the field of computational linguistics, EACL welcomes European and international researchers covering a broad spectrum of research areas that are concerned with computational approaches to natural language. 
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As the flagship European conference in the field of computational linguistics, EACL welcomes European and international researchers covering a broad spectrum of research areas that are concerned with computational approaches to natural language.

workshop paper

The automatic identification of offensive language such as hate speech is important to keep discussions civil in online communities. Identifying hate speech in multimodal content is a particularly challenging task because offensiveness can be manifested in either words or images or a juxtaposition of the two. This paper presents the MasonPerplexity submission for the Shared Task on Multimodal Hate Speech Event Detection at CASE 2024 at EACL 2024. The task is divided into two sub-tasks: sub-task A focuses on the identification of hate speech and sub-task B focuses on the identification of targets in text-embedded images during political events. We use an XLM-roBERTa-large model for sub-task A and an ensemble approach combining XLM-roBERTa-base, BERTweet-large, and BERT-base for sub-task B. Our approach obtained 0.8347 F1-score in sub-task A and 0.6741 F1-score in sub-task B ranking 3rd on both sub-tasks.

MasonPerplexity at Multimodal Hate Speech Event Detection 2024: Hate Speech and Target Detection Using Transformer Ensembles

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.

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

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.

AAST-NLP at Multimodal Hate Speech Event Detection 2024 : A Multimodal Approach for Classification of Text-Embedded Images Based on CLIP and BERT-Based Models.

The escalating impact of climate change on our environment and lives has spurred a global surge in climate change activism. However, the misuse of social media platforms like Twitter has opened the door to the spread of hatred against activism, targeting individuals, organizations, or entire communities. Also, the identification of the stance in a tweet holds paramount significance, especially in the context of understanding the success of activism. So, to address the challenge of detecting such hate tweets, identifying their targets, and classifying stances from tweets, this shared task introduced three sub-tasks, each aiming to address exactly one mentioned issue. We participated in all three sub-tasks and in this paper, we showed a comparative analysis between the different machine learning (ML), deep learning (DL), hybrid, and transformer models. Our approach involved proper hyper-parameter tuning of models and effectively handling class imbalance datasets through data oversampling. Notably, our fine-tuned m-BERT achieved a macro-average f1 score of 0.91 in sub-task A (Hate Speech Detection) and 0.74 in sub-task B (Target Identification). On the other hand, Climate-BERT achieved a f1 score of 0.67 in sub-task C. These scores positioned us at the forefront, securing 1st, 6th, and 15th ranks in the respective sub-tasks. The detailed implementation information for the tasks is available in the GitHub.

CUET_Binary_Hackers at ClimateActivism 2024: A Comprehensive Evaluation and Superior Performance of Transformer-Based Models in Hate Speech Event Detection and Stance Classification for Climate Activism

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.

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

This study details our approach for the CASE 2024 Shared Task on Climate Activism Stance and Hate Event Detection, focusing on Hate Speech Detection, Hate Speech Target Identification, and Stance Detection as classification challenges. We explored the capability of Large Language Models (LLMs), particularly GPT-4, in zero- or few-shot settings enhanced by retrieval augmentation and re-ranking for Tweet classification. Our goal was to determine if LLMs could match or surpass traditional methods in this context. We conducted an ablation study with LLaMA for comparison, and our results indicate that our models significantly outperformed the baselines, securing second place in the Target Detection task. The code for our submission is available at https://github.com/NaiveNeuron/bryndza-case-2024

Bryndza at ClimateActivism 2024: Stance, Target and Hate Event Detection via Retrieval-Augmented GPT-4 and LLaMA

This work presents a systematic search of various model architecture configurations and data cleaning methods. The study evaluates the impact of data cleaning methods on the obtained results. Additionally, we demonstrate that a combination of CNN and Encoder-only models such as BERTweet outperforms FNNs. Moreover, by utilizing data augmentation, we are able to overcome the challenge of data imbalance.

IUST at ClimateActivism 2024: Towards Optimal Stance Detection: A Systematic Study of Architectural Choices and Data Cleaning Techniques

Social media platforms like Twitter - recently rebranded as X - produce nearly half a billion tweets daily and host a significant number of users that can be affected by content that are not properly moderated. In this work, we present an approach that ranked third at the HSD-2Lang 2024 competition's subtask-A along with additional methodology developed for this task and evaluation of different approaches. We utilize three different models and the best performing approach use publicly-available TurkishBERTweet model with low-rank adaptation (LoRA) for fine tuning. We also experiment with another publicly available model and a novel methodology to ensemble different hand-crafted features and outcomes of different models. Finally, we report the experimental results, competition scores, and discussion to improve this effort further.

VRLLab at HSD-2Lang 2024: Turkish Hate Speech Detection Online with TurkishBERTweet

Over the past years, researchers across the globe have made significant efforts to develop systems capable of identifying the presence of hate speech in different languages. This paper describes the team Transformers' submission to the subtasks: Hate Speech Detection in Turkish across Various Contexts and Hate Speech Detection with Limited Data in Arabic, organized by HSD-2Lang in conjunction with CASE at EACL 2024. A BERT based architecture was employed in both the subtasks. We achieved an F1 score of 0.63258 using XLM RoBERTa and 0.48101 using mBERT, hence securing the 6th rank and the 5th rank in the first and the second subtask, respectively.

Transformers at HSD-2Lang 2024: Hate Speech Detection in Arabic and Turkish Tweets Using BERT Based Architectures

Identifying hate speech is a challenging specialization in the natural language processing field (NLP). Particularly in fields with differing linguistics, it becomes more demanding to construct a well-performing classifier for the betterment of the community. In this paper, we leveraged the performances of pre-trained models on the given hate speech detection dataset. By conducting a hyperparameter search, we computed the feasible setups for fine-tuning and trained effective classifiers that performed well in both subtasks in the HSD-2Lang 2024 contest.

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Next from EACL 2024

MasonPerplexity at Multimodal Hate Speech Event Detection 2024: Hate Speech and Target Detection Using Transformer Ensembles

Similar lecture

HAMiSoN-Generative at ClimateActivism 2024: Stance Detection using generative large language models