Malta

This paper introduces a zero-shot hate detection experiment using a multimodal large model. Although the implemented model comprises an unsupervised method, results demonstrate that its performance is comparable to previous supervised methods. Furthemore, this study proposed experiments with various prompts and demonstrated that simpler prompts, as opposed to the commonly used detailed prompts in large language models, led to better performance for multimodal hate speech event detection tasks. While supervised methods offer high performance, they require significant computational resources for training, and the approach proposed here can mitigate this issue. The code is publicly available at https://github.com/yamagishi0824/zeroshot-hate-detect.

EACL 2024

YYama@Multimodal Hate Speech Event Detection 2024: Simpler Prompts, Better Results - Enhancing Zero-shot Detection with a Large Multimodal Model

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multimodal large model

hate detection

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

This paper describes the system that participated in the Climate Activism Stance and Hate Event Detection shared task organized at The 7th Workshop on Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE 2024). The system tackles the important task of hate speech detection by combining large language model predictions with manually designed features, while trying to explain where the LLM approach fails to predict the correct results.

RACAI at ClimateActivism 2024: Improving Detection of Hate Speech by Extending LLM Predictions with Handcrafted Features

In the context of the proliferation of multimodal hate speech related to the Russia-Ukraine conflict, we introduce a unified multimodal fusion system for detecting hate speech and its targets in text-embedded images. Our approach leverages the Twitter-based RoBERTa and Swin Transformer V2 models to encode textual and visual modalities, and employs the Multilayer Perceptron (MLP) fusion mechanism for classification. Our system achieved macro F1 scores of 87.27 for hate speech detection and 80.05 for hate speech target detection in the Multimodal Hate Speech Event Detection Challenge 2024, securing the 1st rank in both subtasks. We open-source the trained models at https://huggingface.co/Yestin-Wang

CLTL@Multimodal Hate Speech Event Detection 2024: The Winning Approach to Detecting Multimodal Hate Speech and Its Targets

CASE in EACL 2024 proposes the shared task on Hate Speech and Stance Detection during Climate Activism. In our participation in the stance detection task, we have tested different approaches using LLMs for this classification task. We have tested a generative model using the classical seq2seq structure. Subsequently, we have considerably improved the results by replacing the last layer of these LLMs with a classifier layer. We have also studied how the performance is affected by the amount of data used in training. For this purpose, a partition of the dataset has been used and external data from posture detection tasks has been added.

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

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.

HAMiSoN-MTL at ClimateActivism 2024: Detection of Hate Speech, Targets, and Stance using Multi-task Learning

The paper presents the approach developed for the "Climate Activism Stance and Hate Event Detection" Shared Task at CASE 2024, comprising three sub-tasks. The Shared Task aimed to create a system capable of detecting hate speech, identifying the targets of hate speech, and determining the stance regarding climate change activism events in English tweets. The approach involved data cleaning and pre-processing, addressing data imbalance, and fine-tuning the "mistralai/Mistral-7B-v0.1" LLM for sequence classification using PEFT (Parameter-Efficient Fine-Tuning). The LLM was fine-tuned using two PEFT methods, namely LoRA and prompt tuning, for each sub-task, resulting in the development of six Mistral-7B fine-tuned models in total. Although both methods surpassed the baseline model scores of the task organizers, the prompt tuning method yielded the highest results. Specifically, the prompt tuning method achieved a Macro-F1 score of 0.8649, 0.6106 and 0.6930 in the test data of sub-tasks A, B and C, respectively.

NLPDame at ClimateActivism 2024: Mistral Sequence Classification with PEFT for Hate Speech, Targets and Stance Event Detection

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.

AAST-NLP at ClimateActivism 2024: Ensemble-Based Climate Activism Stance and Hate Speech Detection : Leveraging Pretrained Language Models

Social media users often express hate speech towards specific targets and may either support or refuse activist movements. The automated detection of hate speech, which involves identifying both targets and stances, plays a critical role in event identification to mitigate its negative effects. In this paper, we present our methods for three subtasks of the Climate Activism Stance and Hate Event Detection Shared Task at CASE 2024. For each subtask (i) hate speech identification (ii) targets of hate speech identification (iii) stance detection, we experiment with optimized Transformer-based architectures that focus on tweet-specific features such as hashtags, URLs, and emojis. Furthermore, we investigate generative large language models, such as Llama2, using specific prompts for the first two subtasks. Our experiments demonstrate better performance of our models compared to baseline models in each subtask. Our solutions also achieve third, fourth, and first places respectively in the subtasks.

ARC-NLP at ClimateActivism 2024: Stance and Hate Speech Detection by Generative and Encoder Models Optimized with Tweet-Specific Elements

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.

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

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

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RACAI at ClimateActivism 2024: Improving Detection of Hate Speech by Extending LLM Predictions with Handcrafted Features

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CLTL@Multimodal Hate Speech Event Detection 2024: The Winning Approach to Detecting Multimodal Hate Speech and Its Targets

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