United States

Social media has become a widely used platform for communication and entertainment, but it has also become a space where abuse and harassment can thrive. Women, in particular, face hateful and abusive comments that reflect gender inequality. This paper discusses our participation in the Abusive Text Targeting Women in Dravidian Languages shared task at DravidianLangTech@NAACL 2025, which focuses on detecting abusive text targeting women in Malayalam social media comments. The shared task provided a dataset of YouTube comments in Tamil and Malayalam, focusing on sensitive and controversial topics where abusive behavior is prevalent. Our participation focused on the Malayalam dataset, where the goal was to classify comments into these categories accurately. Malayalam-BERT achieved the best performance on the subtask, securing 3rd place with a macro f1-score of 0.7083, highlighting the effectiveness of transformer models for low-resource languages. These results contribute to tackling gender-based abuse and improving online content moderation for underrepresented languages.

NAACL 2025

CUET_Novice@DravidianLangTech 2025: Abusive Comment Detection in Malayalam Text Targeting Women on Social Media Using Transformer-Based Models

malayalam-bert

youtube comments

malayalam

dravidian languages

abusive language

social media

### 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. 
**[Continue reading...](https://drive.google.com/file/d/1jX-qGhqVSZZCIrAnJaz798pflu5Irrdn/view?usp=sharing)**

*- 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

The rise of social media has significantly facilitated the rapid spread of hate speech. Detecting hate speech for content moderation is challenging, especially in low-resource languages (LRLs) like Telugu. Although some progress has been noticed in hate speech detection in Telegu concerning unimodal (text or image) in recent years, there is a lack of research on hate speech detection based on multimodal content detection (specifically using audio and text). In this regard, DravidianLangTech has arranged a shared task to address this challenge. This work explored three machine learning (ML), three deep learning (DL), and seven transformer-based models that integrate text and audio modalities using cross-modal attention for hate speech detection. The evaluation results demonstrate that mBERT achieved the highest F-1 score of 49.68% using text. However, the proposed multimodal attention-based approach with Whisper-small+TeluguBERT-3 achieved an F-1 score of 43 68%, which helped us achieve a rank of 3rd in the shared task competition.

SemanticCuetSync@DravidianLangTech 2025: Multimodal Fusion for Hate Speech Detection - A Transformer Based Approach with Cross-Modal Attention

Political sentiment analysis in multilingual content poses significant challenges in capturing the subtle variations of diverse sentiments expressed in complex and low-resourced languages. Accurately classifying sentiments, whether positive, negative, or neutral, is crucial for understanding public discourse. A shared task on Political Multiclass Sentiment Analysis of Tamil X (Twitter) Comments, organized by DravidianLangTech@NAACL 2025, provided an opportunity to tackle these challenges. For this task, we implemented two data augmentation techniques, which are synonym replacement and back translation, and then explored various machine learning (ML) algorithms, including Logistic Regression, Decision Tree, Random Forest, SVM, and MultiNomial Naive Bayes. To capture the semantic meanings more efficiently, we experimented with deep learning (DL) models, including GRU, BiLSTM, BiGRU, and a hybrid CNN-BiLSTM. The Bidirectional Gated Recurrent Unit (BiGRU) achieved the best macro-F1 (MF1) score of 0.33, securing the 17th position in the shared task. These findings underscore the challenges of political sentiment analysis in low-resource languages and the need for advanced language-specific models for improved classification.

CUET_Novice@DravidianLangTech 2025: A Bi-GRU Approach for Multiclass Political Sentiment Analysis of Tamil Twitter (X) Comments

Misogyny memes are a form of online content that spreads harmful and damaging ideas about women. By combining images and text, they often aim to mock, disrespect, or insult women, sometimes overtly and other times in more subtle, insidious ways. Detecting Misogyny memes is crucial for fostering safer and more respectful online communities. While extensive research has been conducted on high-resource languages (HRLs) like English, low-resource languages (LRLs) such as Dravidian (e.g., Tamil and Malayalam) remain largely overlooked. The shared task on Misogyny Meme Detection, organized as part of DravidianLangTech@NAACL 2025, provided a platform to tackle the challenge of identifying misogynistic content in memes, specifically in Malayalam. We participated in the competition and adopted a multimodal approach to contribute to this effort. For image analysis, we employed a ResNet18 model to extract visual features, while for text analysis, we utilized the IndicBERT model. Our system achieved an impressive F1-score of 0.87, earning us the 3^rd rank in the task.

One_by_zero@DravidianLangTech 2025: A Multimodal Approach for Misogyny Meme Detection in Malayalam Leveraging Visual and Textual Features

Misogyny memes, a form of digital content, reflect societal prejudices by discriminating against women through shaming and stereotyping. In this study, we present a multimodal approach combining Indic-BERT and ViT-base-patch16-224 to address misogyny memes. We explored various Machine Learning, Deep Learning, and Transformer models for unimodal and multimodal classification using provided Tamil and Malayalam meme dataset. Our findings highlight the challenges traditional ML and DL models face in understanding the nuances of Dravidian languages, while emphasizing the importance of transformer models in capturing these complexities. Our multimodal method achieved F1-scores of 77.18% and 84.11% in Tamil and Malayalam, respectively, securing 6th place for both languages among the participants.

CUET-NLP_MP@DravidianLangTech 2025: A Transformer-Based Approach for Bridging Text and Vision in Misogyny Meme Detection in Dravidian Languages

E-commerce platforms face growing challenges regarding consumer trust and review authenticity because of the growing number of AI-generated product reviews. Low-resource languages (LRLs) such as Tamil and Malayalam face limited investigation by AI detection techniques because these languages experience constraints from sparse data sources and complex linguistic structures. The research team at CUET_NetworkSociety took part in the AI-Generated Review Detection contest during the DravidianLangTech@NAACL 2025 event to fill this knowledge void. Using a combination of machine learning, deep learning, and transformer-based models, we detected AI-generated and human-written reviews in both Tamil and Malayalam. The developed method employed DistilBERT, which underwent an advanced preprocessing pipeline and hyperparameter optimization using the Transformers library. This approach achieved a Macro F1-score of 0.81 for Tamil (Subtask 1), securing 18th place, and a score of 0.7287 for Malayalam (Subtask 2), ranking 25th.

CUET_NetworkSociety@DravidianLangTech 2025: A Transformer-Based Approach to Detecting AI-Generated Product Reviews in Low-Resource Dravidian Languages

Memes are commonly used for communication on social media platforms, and some of them can propagate misogynistic content, spreading harmful messages. Detecting such misogynistic memes has become a significant challenge, especially for low-resource languages like Tamil and Malayalam, due to their complex linguistic structures. To tackle this issue, a shared task on detecting misogynistic memes was organized at DravidianLangTech@NAACL 2025. This paper proposes a multimodal deep learning approach for detecting misogynistic memes in Tamil and Malayalam. The proposed model combines fine-tuned ResNet18 for visual feature extraction and indicBERT for analyzing textual content. The fused model was applied to the test dataset, achieving macro F1 scores of 76.32% for Tamil and 80.35% for Malayalam. Our approach led to 7^th and 12^th positions for Tamil and Malayalam, respectively.

CUET_NetworkSociety@DravidianLangTech 2025: A Multimodal Framework to Detect Misogyny Meme in Dravidian Languages

Social media has transformed communication, but it has opened new ways for women to be abused. Because of complex morphology, large vocabulary, and frequent code-mixing of Tamil and Malayalam, it might be especially challenging to identify discriminatory text in linguistically diverse settings. Because traditional moderation systems frequently miss these linguistic subtleties, gendered abuse in many forms—from outright threats to character insults and body shaming—continues. In addition to examining the sociocultural characteristics of this type of harassment on social media, this study compares the effectiveness of several Natural Language Processing (NLP) models, such as FastText, transformer-based architectures, and BiLSTM. Our results show that FastText achieved an macro f1 score of 0.74 on the Tamil dataset and 0.64 on the Malayalam dataset, outperforming the Transformer model which achieved a macro f1 score of 0.62 and BiLSTM achieved 0.57. By addressing the limitations of existing moderation techniques, this research underscores the urgent need for language-specific AI solutions to foster safer digital spaces for women.

Cyber Protectors@DravidianLangTech 2025: Abusive Tamil and Malayalam Text Targeting Women on Social Media using FastText

Social media has become an established medium of public communication and opinions on every aspect of life, but especially politics. This has resulted in a growing need for tools that can process the large amount of unstructured data that is produced on these platforms providing actionable insights in domains such as social trends and political opinion. Low-resource languages like Tamil present challenges due to limited tools and annotated data, highlighting the need for NLP focus on understudied languages. To address this, a shared task has been organized by DravidianLangTech@NAACL 2025 for political sentiment analysis for low-resource languages, with a specific focus on Tamil. In this task, we have explored several machine learning methods such as SVM, AdaBoost, GB, deep learning methods including CNN, LSTM, GRU BiLSTM, and the ensemble of different deep learning models, and transformer-based methods including mBERT, T5, XLM-R. The mBERT model performed best by achieving a macro F1 score of 0.2178 and placing our team 22^nd in the rank list.

CUET_NetworkSociety@DravidianLangTech 2025: A Transformer-Driven Approach to Political Sentiment Analysis of Tamil X (Twitter) Comments

The growing use of social communication platforms has seen women facing higher degrees of online violence than ever before. This paper presents how a deep learning abuse detection system can be applied to inappropriate text directed at women on social media. Because of the diversity of languages and the casual nature of online communication, coupled with the cultural diversity around the world, the detection of such content is often severely lacking. This research utilized Long Short-Term Memory (LSTM) for abuse text detection in Malayalam and Tamil languages. This modeldelivers 0.75, a high F1 score for Malayalam, and for Tamil, 0.72, achieving the desired balance of identifying abuse and non-abusive content and achieving high-performance rates. The designed model, based on the dataset provided in DravidianLangTech@NAACL2025 (shared task) comprising code-mixed abusive and nonabusive social media posts in Malayalam and Tamil, showcases a high propensity for detecting accuracy and indicates the likely success of deep learning-based models for abuse text detection in resource-constrained languages.

DLTCNITPY@DravidianLangTech 2025 Abusive Code-mixed Text Detection System Targeting Women for Tamil and Malayalam Languages using Deep Learning Technique

Detecting AI-generated product reviews is a critical challenge in natural language processing (NLP), particularly for low-resource languages like Malayalam. In this study, we propose a large language model (LLM)-based approach to identify AI-generated reviews in Dravidian languages, focusing on the product review domain. We systematically evaluated multiple LLMs on a dedicated Malayalam dataset to assess their effectiveness in distinguishing between human-written and AI-generated reviews. Our experiments demonstrate that the Gemma-2B model outperforms other models, achieving a macro F1-score of 89.99%. Our approach secured 5^{th} place in the DravidianLangTech@NAACL 2025 shared task for Malayalam, highlighting the potential of LLMs in tackling the challenges of AI-generated review detection in low-resource languages. Our findings highlight the potential of LLMs in detecting AI-generated content in underrepresented languages, contributing to advancements in Dravidian language processing and the broader field of AI-generated content identification.

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SemanticCuetSync@DravidianLangTech 2025: Multimodal Fusion for Hate Speech Detection - A Transformer Based Approach with Cross-Modal Attention

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