United States

Social media is a powerful communication tool and rich in diverse content requiring innovative approaches to understand nuances of the lan- guages. Addressing challenges like hate speech necessitates multimodal analysis that integrates textual, and other cues to capture its context and intent effectively. This paper proposes a multimodal hate speech detection system in Tamil, which uses textual and audio features for classification. Our proposed system uses a fine-tuned Indic-BERT model for text based hate speech detection and Wav2Vec2 model for audio based hate speech detection of au- dio data. The fine-tuned Indic-BERT model with Whisper achieved an F1 score of 0.25 on Multimodal approach. Our proposed approach ranked at 10th position in the shared task on Multimodal Hate Speech Detection in Dravid- ian languages at the NAACL 2025 Workshop DravidianLangTech.

NAACL 2025

DLRG@DravidianLangTech 2025: Multimodal Hate Speech Detection in Dravidian Languages

mfcc features

speech-text fusion

random forest.

malayalam

dravidian languages

bilstm

telugu

capsule networks

tamil

multilingual nlp

hate speech detection

### Welcome to 2025 Annual Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics

Welcome to the 2025 meeting of the Nations of the Americas Chapter of the Association for Computational Linguistics! I am proud to help organize the first NAACL conference to carry the new name of our organization, one that emphasizes inclusion for all of the Americas. I am also pleased to welcome you to Albuquerque, New Mexico, a state whose unique blend of cultural influences will make for an excellent backdrop for NAACL 2025, especially with this year’s special theme on NLP in a Multicultural World. 
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*- Colin Cherry, Google, NAACL 2025 General Chair* 

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Welcome to 2025 Annual Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics

workshop paper

Ensuring a safe and inclusive online environment requires effective hate speech detection on social media. While detection systems have significantly advanced for English, many regional languages, including Malayalam, Tamil and Telugu, remain underrepresented, creating challenges in identifying harmful content accurately. These languages present unique challenges due to their complex grammar, diverse dialects, and frequent code-mixing with English. The rise of multimodal content, including text and audio, adds further complexity to detection tasks. The shared task ``Multimodal Hate Speech Detection in Dravidian Languages: DravidianLangTech@NAACL 2025'' has aimed to address these challenges. A Youtube-sourced dataset has been provided, labeled into five categories: Gender (G), Political (P), Religious (R), Personal Defamation (C) and Non-Hate (NH). In our approach, we have used mBERT, T5 for text and Wav2Vec2 and Whisper for audio. T5 has performed poorly compared to mBERT, which has achieved the highest F1 scores on the test dataset. For audio, Wav2Vec2 has been chosen over Whisper because it processes raw audio effectively using self-supervised learning. In the hate speech detection task, we have achieved a macro F1 score of 0.2005 for Malayalam, ranking 15th in this task, 0.1356 for Tamil and 0.1465 for Telugu, with both ranking 16th in this task.

Team ML_Forge@DravidianLangTech 2025: Multimodal Hate Speech Detection in Dravidian Languages

Abusive comment detection in low-resource languages is a challenging task particularly when addressing gender-based abuse. Iden tifying abusive language targeting women is crucial for effective content moderation and fostering safer online spaces. A shared task on abusive comment detection in Tamil text organized by DravidianLangTech@NAACL 2025 allowed us to address this challenge using a curated dataset. For this task, we experimented with various machine learning (ML) and deep learning (DL) models includ ing Logistic Regression, Random Forest, SVM, CNN, LSTM, BiLSTMand transformer-based models such as mBERT, IndicBERT, XLM RoBERTa and many more. The dataset com prised of Tamil YouTube comments anno tated with binary labels, Abusive and Non Abusive capturing explicit abuse, implicit bi ases and stereotypes. Our experiments demon strated that XLM-RoBERTa achieved the high est macro F1-score(0.80), highlighting its effec tiveness in handling Tamil text. This research contributes to advancing abusive language de tection and natural language processing in low resource languages particularly for addressing gender-based abuse online.

CUET_Ignite@DravidianLangTech-NAACL2025: Detection of Abusive Comments in Tamil Text Using Transformer Models

Artificial Intelligence (AI) is opening new doors of learning and interaction. However, it has its share of problems. One major issue is the ability of AI to generate text that resembles human-written text. So, how can we tell apart human-written text from AI-generated text? With this in mind, we have worked on detecting AI-generated product reviews in Dravidian languages, mainly in Malayalam and Tamil. The ``Shared Task on Detecting AI-Generated Product Reviews in Dravidian Languages,'' held as part of the DravidianLangTech Workshop at NAACL 2025 has provided a dataset categorized into two categories, human-written review and AI-generated review. We have implemented four machine learning models (Random Forest, Support Vector Machine, Decision Tree, and XGBoost), four deep learning models (Long Short-Term Memory, Bidirectional Long Short-Term Memory, Gated Recurrent Unit, and Recurrent Neural Network), and three transformer-based models (AI-Human-Detector, Detect-AI-Text, and E5-Small-Lora-AI-Generated-Detector). We have conducted a comparative study among all the models by training and evaluating each model on the dataset. We have discovered that the transformer, E5-Small-Lora-AI-Generated-Detector, has provided the best result with an F1 score of 0.8994 on the test set ranking 7th position in the Malayalam language. Tamil has a higher token overlap and richer morphology than Malayalam. Thus, we obtained a worse F1 score of 0.5877 ranking 28th position in the Tamil language among all participants in the shared task.

CUET_Absolute_Zero@DravidianLangTech 2025: Detecting AI-Generated Product Reviews in Malayalam and Tamil Language Using Transformer Models

This study focuses on developing a solution for detecting abusive texts on social media against women in Tamil and Malayalam, two low-resource Dravidian languages in South India. As the usage of social media for communication and idea sharing has increased significantly, these platforms are being used to target and victimize women. Hence an automated solution becomes necessary to screen the huge volume of content generated. This work is part of the shared Task on Abusive Tamil and Malayalam Text targeting Women on Social Media DravidianLangTech@NAACL 2025. The approach used to tackle this problem involves utilizing LLM based techniques for classifying abusive text. The Macro Average F1-Score for the Tamil BERT model was 0.76 securing the 11th position, while the Malayalam BERT model for Malayalam obtained a score of 0.30 and secured the 33rd rank. The proposed solution can be extended further to incorporate other regional languages as well based on similar techniques.

SSNTrio@DravidianLangTech2025: LLM Based Techniques for Detection of Abusive Text Targeting Women

Fake news detection is a critical problem in today's digital age, aiming to classify intentionally misleading or fabricated news content. In this study, we present a transformer- and LLM-based ensemble method to address the challenges in fake news detection. We explored various machine learning (ML), deep learning (DL), transformer, and LLM-based approaches on a Malayalam fake news detection dataset. Our findings highlight the difficulties faced by traditional ML and DL methods in accurately detecting fake news, while transformer- and LLM-based ensemble methods demonstrate significant improvements in performance. The ensemble method combining Sarvam-1, Malayalam-BERT, and XLM-R outperformed all other approaches, achieving an F1-score of 89.30% on the given dataset. This accomplishment, which contributed to securing 2^{nd} place in the shared task at DravidianLangTech 2025, underscores the importance of developing effective methods for detecting fake news in Dravidian languages.

CUET-NLP_MP@DravidianLangTech 2025: A Transformer and LLM-Based Ensemble Approach for Fake News Detection in Dravidian

Memes have become one of the main mediums for expressing ideas, humor, and opinions through visual-textual content on social media. The same medium has been used to propagate harmful ideologies, such as misogyny, that undermine gender equality and perpetuate harmful stereotypes. Identifying misogynistic memes is particularly challenging in low-resource languages (LRLs), such as Tamil and Malayalam, due to the scarcity of annotated datasets and sophisticated tools. Therefore, DravidianLangTech@NAACL 2025 launched a Shared Task on Misogyny Meme Detection to identify misogyny memes. For this task, this work exploited an extensive array of models, including machine learning (LR, RF, SVM, and XGBoost), and deep learning (CNN, BiLSTM+CNN, CNN+GRU, and LSTM) are explored to extract textual features, while CNN, BiLSTM + CNN, ResNet50, and DenseNet121 are utilized for visual features. Furthermore, we have explored feature-level and decision-level fusion techniques with several model combinations like MuRIL with ResNet50, MuRIL with BiLSTM+CNN, T5+MuRIL with ResNet50, and mBERT with ResNet50. The evaluation results demonstrated that BERT + ResNet50 performed best, obtaining an F1 score of 0.81716 (Tamil) and were ranked 2^nd in the task. The early fusion of MuRIL+ResNet50 showed the highest F1 score of 0.82531 and received a 9^th rank in Malayalam.

CUET-NLP_Big_O@DravidianLangTech 2025: A Multimodal Fusion-based Approach for Identifying Misogyny Memes

The rapid growth of digital platforms and social media has significantly contributed to spreading fake news, posing serious societal challenges. While extensive research has been conducted on detecting fake news in high-resource languages (HRLs) such as English, relatively little attention has been given to low-resource languages (LRLs) like Malayalam due to insufficient data and computational tools. To address this challenge, the DravidianLangTech 2025 workshop organized a shared task on fake news detection in Dravidian languages. The task was divided into two sub-tasks, and our team participated in Task 1, which focused on classifying social media texts as original or fake. We explored a range of machine learning (ML) techniques, including Logistic Regression (LR), Multinomial Naïve Bayes (MNB), and Support Vector Machines (SVM), as well as deep learning (DL) models such as CNN, BiLSTM, and a hybrid CNN+BiLSTM. Additionally, this work examined several transformer-based models, including m-BERT, Indic-BERT, XLM-Roberta, and MuRIL-BERT, to exploit the task. Our team achieved 6^th place in Task 1, with MuRIL-BERT delivering the best performance, achieving an F1 score of 0.874.

CUET-NLP_Big_O@DravidianLangTech 2025: A BERT-based Approach to Detect Fake News from Malayalam Social Media Texts

This paper presents the approach and findings from the Multimodal Social Media Data Analysis in Dravidian Languages (MSMDA-DL) shared task at DravidianLangTech@NAACL 2025. The task focuses on detecting multimodal hate speech in Tamil, Malayalam, and Telugu, requiring models to analyze both text and speech components from social media content. The proposed methodology uses language-specific BERT models for the provided text transcripts, followed by multimodal feature extraction techniques, and classification using a Random Forest classifier to enhance performance across the three languages. The models achieved a macro-F1 score of 0.7332 (Rank 1) in Tamil, 0.7511 (Rank 1) in Malayalam, and 0.3758 (Rank 2) in Telugu, demonstrating the effectiveness of the approach in multilingual settings. The models performed well despite the challenges posed by limited resources, highlighting the potential of language-specific BERT models and multimodal techniques in hate speech detection for Dravidian languages.

SSNTrio @ DravidianLangTech 2025: Hybrid Approach for Hate Speech Detection in Dravidian Languages with Text and Audio Modalities

The rapid spread of misinformation in the digital era presents critical challenges for fake news detection, especially in low-resource languages (LRLs) like Malayalam, which lack extensive datasets and pre-trained models for widely spoken languages. This gap in resources makes it harder to build robust systems for combating misinformation despite the significant societal and political consequences it can have. To address these challenges, this work proposes a transformer-based approach for Task 1 of the Fake News Detection in Dravidian Languages (DravidianLangTech@NAACL 2025), which focuses on classifying Malayalam social media texts as either *original* or *fake*. The experiments involved a range of ML techniques (Logistic Regression (LR), Support Vector Machines (SVM), and Decision Trees (DT)) and DL architectures (BiLSTM, BiLSTM-LSTM, and BiLSTM-CNN). This work also explored transformer-based models, including IndicBERT, MuRiL, XLM-RoBERTa, and Malayalam BERT. Among these, Malayalam BERT achieved the best performance, with the highest macro F1-score of 0.892, securing a rank of 3^rd in the competition.

One_by_zero@DravidianLangTech 2025: Fake News Detection in Malayalam Language Leveraging Transformer-based Approach

Memes, combining images and text, are a popular social media medium that can spread humor or harmful content, including misogyny—hatred or discrimination against women. Detecting misogynistic memes in Malayalam is challenging due to their multimodal nature, requiring analysis of both visual and textual elements. A Shared Task on Misogyny Meme Detection, organized as part of DravidianLangTech@NAACL 2025, aimed to address this issue by promoting the advancement of multimodal machine learning models for classifying Malayalam memes as misogynistic or non-misogynistic. In this work, we explored visual, textual, and multimodal approaches for meme classification. CNN, ResNet50, Vision Transformer (ViT), and Swin Transformer were used for visual feature extraction, while mBERT, IndicBERT, and MalayalamBERT were employed for textual analysis. Additionally, we experimented with multimodal fusion models, including IndicBERT+ViT, MalayalamBERT+ViT, and MalayalamBERT+Swin. Among these, our MalayalamBERT+Swin Transformer model performed best, achieving the highest weighted F1-score of 0.87631, securing 1st place in the competition. Our results highlight the effectiveness of multimodal learning in detecting misogynistic Malayalam memes and the need for robust AI models in low-resource languages.

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