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The rapid proliferation of digital platforms has intensified online hate speech, especially in low-resource languages such as Tamil, where automated moderation techniques remain underdeveloped. This paper presents a three-stage methodology for generating counter narratives in Tamil. First, a seed dataset of 220 hate speech counter narrative (HS-CN) pairs is expanded to 5,000 through a human-in-the-loop Author Reviewer framework with expert validation. Second, a fact-based retrieval augmented generation (RAG) system is employed to incorporate external knowledge to enhance factual accuracy and persuasiveness. Finally, the human post-edited dataset is integrated to the RAG system as a curated knowledge base yielding a Fact-RAG system with stronger factual grounding and cultural appropriateness. Assessment through intrinsic indicators and LLM-based evaluations indicates that our methodology generates counter-narratives that are varied, credible, and contextually relevant.  These findings underscore the effectiveness of integrating human supervision, factual validation, and selected examples for counter-narrative development in low-resource settings.\n                    <jats:bold>GitHub:<\/jats:bold>\n                    <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"https:\/\/github.com\/Bharathi-AI-for-Social-Good\/Fact-RAG-BasedCN-Ta\" ext-link-type=\"uri\">https:\/\/github.com\/Bharathi-AI-for-Social-Good\/Fact-RAG-BasedCN-Ta<\/jats:ext-link>\n                  <\/jats:p>","DOI":"10.1007\/s41060-026-01023-x","type":"journal-article","created":{"date-parts":[[2026,2,6]],"date-time":"2026-02-06T19:27:34Z","timestamp":1770406054000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Nalvakku: fact-based counter narratives for Tamil hate speech"],"prefix":"10.1007","volume":"22","author":[{"given":"Narendran","family":"Ravikumar","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Prasanna Kumar","family":"Kumaresan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Saranya","family":"Rajiakodi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Subalalitha","family":"CN","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bharathi Raja","family":"Chakravarthi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,2,6]]},"reference":[{"issue":"11","key":"1023_CR1","doi-asserted-by":"publisher","first-page":"7893","DOI":"10.1080\/03772063.2022.2043786","volume":"69","author":"S Anbukkarasi","year":"2023","unstructured":"Anbukkarasi, S., Varadhaganapathy, S.: Deep learning-based hate speech detection in code-mixed tamil text. 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