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home / papers / 2026 / SLANG-GraphRAG: Multi-Layered Retrieval with Domain-Specific Knowledge for Low Resource Social Media Conversations

SLANG-GraphRAG: Multi-Layered Retrieval with Domain-Specific Knowledge for Low Resource Social Media Conversations

Paper · 2026 · Ifeoluwa Wuraola, Daniel Marciniak and Nina Dethlefs

Summary

Large language models often misread emotion in social media posts that rely on regional slang, and the problem is worse for under-resourced varieties such as Nigerian English and Pidgin. The authors propose SLANG-GraphRAG, which supplies LLMs with culture-specific knowledge from a knowledge graph during one-shot emotion classification. They collected geo-tagged climate-related tweets from the UK and Nigeria and built a slang dictionary of about 240 terms from regional websites and Urban Dictionary. They then filtered for tweets with genuine slang usage. A sample of these tweets was labelled by local annotators with seven emotions. The graph links each slang term to its meaning, its regional usage, and the parent tweet in a conversation. Retrieval depth increases from one to three hops. Graph-based retrieval beat zero-shot prompting and plain dictionary RAG, with accuracy gains of up to 31% and F1 gains of up to 28%. The authors also propose a probabilistic metric that compares model outputs against the full distribution of human labels rather than a single gold label. The work shows that structured cultural knowledge helps LLMs with subjective tasks in low-resource settings.

Key points

  • Data: 138,862 geo-tagged tweets (2010–March 2024) were collected with climate and nature keywords, sampled evenly from the UK and Nigeria. Slang filtering left 2,576 tweets: 2,003 Nigerian and 573 UK.
  • Annotation: a 517-tweet test set (397 NG / 120 UK) was labelled by nine annotators per region. Annotators picked from seven emotion categories: six specific emotions (Anger, Disgust, Fear, Joy, Sadness, Surprise) plus a Neutral option.
  • Graph and models: the knowledge graph (KG_Info) uses the relations "means", "used_in", and "text_parent". The models tested were LLaMA-3.2-3B and Qwen2-7B-Instruct.
  • Accuracy result: double-hop graph RAG with LLaMA-3.2-3B raised accuracy from 0.45 to 0.59 (+31.1%). Its normalized log-sum rose from 0.52 to 0.73.
  • Conversational context: with prior conversation included, triple-hop retrieval reached normalized log-sum scores of 0.92 (LLaMA) and 0.96 (Qwen). This nearly matches the human majority-label reference of 0.97, compared with 0.57 and 0.36 for zero-shot prompting.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🎓University of HullHull · United Kingdom3 works · 1 citation🎓Loughborough UniversityLoughborough · United Kingdom3 works · 15 citations

 Fields, topics and concepts

📚 Shelves 6The field of science this work belongs to (OpenAlex fields).Computer Science · 18,614 worksArtificial Intelligence · 17,965 worksArts and Humanities · 17,450 worksLanguage and Linguistics · 17,296 worksPsychology · 1,649 worksExperimental and Cognitive Psychology · 930 works

🧭 Topics 3Research subjects and languages the work is about.Sentiment & emotion · 1,035 worksInformation retrieval & search · 552 worksLow-resource languages · 287 works

Cite

Cite as Ifeoluwa Wuraola, Daniel Marciniak and Nina Dethlefs. 2026. SLANG-GraphRAG: Multi-Layered Retrieval with Domain-Specific Knowledge for Low Resource Social Media Conversations. Findings of the Association for Computational Linguistics: EACL 2026.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2026.findings-eacl.203

@inproceedings{wuraola2026slang,
  title = {{SLANG-GraphRAG: Multi-Layered Retrieval with Domain-Specific Knowledge for Low Resource Social Media Conversations}},
  author = {Ifeoluwa Wuraola, Daniel Marciniak and Nina Dethlefs},
  booktitle = {Findings of the Association for Computational Linguistics: EACL 2026},
  year = {2026},
  doi = {10.18653/v1/2026.findings-eacl.203},
  url = {https://doi.org/10.18653/v1/2026.findings-eacl.203}
}

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