Online hate speech is spreading rapidly, creating significant challenge, particularly in low-resource language such as Tamil. Lack of developed automated content moderation systems makes it difficult to control harmful content effectively. In this study, we propose a computational framework for generating Counter Narratives (CNs) using classical NLP techniques. With this, we leverage TF-IDF features with n-grams to identify the labels as Homophobic or Transphobic. Span detection is performed with TF-IDF features with n-grams and Machine learning models. Counter narratives are then retrieved by computing cosine similarity, ensuring semantic alignment and contextual relevance. Evaluation on the expanded human curated dataset demonstrates that our approach produces contextually appropriate and semantically coherent counter narratives. Notably, the proposed system is submitted at Task 2 shown a overall average score of 80.40 % for Tamil and 77.29 % for English and secured first and fourth rank respectively. GitHub: https://github.com/kannanrrk/Span-Counter-Feature-Based

This paper describes our system developed for the shared task on Dialect Based Speech Recognition and Classification in Tamil at DravidianLangTech@ACL 2026. We participated in both Subtask 1 (Dialect Identification) and Subtask 2 (Dialectal ASR). Our approach leverages a single Tamil-adapted Whisper Medium model as a unified foundation for both tasks. For dialect classification, we have used the Whisper encoder as a feature extractor by discarding the decoder, applying mean pooling over the temporal dimension, and fine-tuning the full encoder with a lightweight classification head, achieving 73.4% accuracy on the test set. For dialectal ASR, we apply Low-Rank Adaptation (LoRA) to the full encoder-decoder architecture with SpecAugment-based data augmentation, achieving a Word Error Rate (WER) of 0.55 on the test set. Our experiments reveal that unfreezing the pre-trained encoder is critical for dialect discrimination, boosting accuracy from 52.78% (frozen) to 73.4% (unfrozen). The code is publicly available at https://github.com/DLRG-VIT/DravidianLangTech2026

This paper presents an overview of the second shared task on Abusive Tamil Text Targeting Women on Social Media as a binary classification problem (abusive vs. non-abusive). We release a dataset of Tamil YouTube comments and evaluate submissions using macro-F1 to encourage balanced performance in a noisy, low-resource setting. There are 89 teams registered for this task and 24 teams submitted the results. The approaches used by the teams includes transformer fine-tuning, heterogeneous ensembles, classical baselines, and large language models using prompting and LoRA. Results show that the best-performing system scored 0.8297 macro-F1 and many submissions are around 0.79-0.81. Across submissions, transformer fine-tuning with domain-aligned encoders is consistently strong, while additional gains are frequently associated with Tamil-aware normalization and macro-F1-oriented calibration such as class-weighted learning and validation-based threshold tuning. Overall, the findings highlights the importance of language-aware preprocessing and careful decision calibration for reliable moderation of women-targeted abusive Tamil social media text.Disclaimer: This paper (including figures and examples) may contain offensive or harmful language, including abusive content targeting women. All such text is presented solely for research and educational purposes and it does not reflect the author’s views. Reader discretion is advised.

Social media is a powerful communication tooland rich in diverse content requiring innovativeapproaches to understand nuances of the lan-guages. Addressing challenges like hate speechnecessitates multimodal analysis that integratestextual, and other cues to capture its contextand intent effectively. This paper proposes amultimodal hate speech detection system inTamil, which uses textual and audio featuresfor classification. Our proposed system usesa fine-tuned Indic-BERT model for text basedhate speech detection and Wav2Vec2 modelfor audio based hate speech detection of au-dio data. The fine-tuned Indic-BERT modelwith Whisper achieved an F1 score of 0.25 onMultimodal approach. Our proposed approachranked at 10th position in the shared task onMultimodal Hate Speech Detection in Dravid-ian languages at the NAACL 2025 WorkshopDravidianLangTech.

Memes became an important way of expressing relevant idea through social media platforms and forums. At the same time, these memes are trolled by a person who tries to get identified from the other internet users like social media users, chat rooms and blogs. The memes contain both textual and visual information. Based on the content of memes, they are trolled in online community. There is no restriction for language usage in online media. The present work focuses on whether memes are trolled or not trolled. The proposed multi modal approach achieved considerably better weighted average F1 score of 0.5437 compared to Unimodal approaches. The other performance metrics like precision, recall, accuracy and macro average have also been studied to observe the proposed system.