Year: 2026
Type: conference-paper
Abstract: Online hate speech is spreading rapidly, creating significant challenge, particularly in lowresource 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
Authors: Ramesh Kannan R, Ratnavel Rajalakshmi
Institution: Vellore Institute of Technology University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
SDG: Reduced inequalities
Open Access status: hybrid
OpenAlex ID: W7166825271