# CBRS: Cognitive Blood Request System with Bilingual Dataset and Dual-Layer Filtering for Multi-Platform Social Streams

arXiv: https://arxiv.org/abs/2604.16665 · HTML: https://mlsanity.com/paper/2604.16665/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 83 references that are in the map.
- First posted on arXiv 17 Apr 2026.

## Builds on (most cited first, 16 of 16)

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- [Geo-knowledge-guided GPT models improve the extraction of location descriptions from disaster-related social media messages](https://mlsanity.com/paper/2310.09340/) (2023)
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- [Rethinking Semantic Parsing for Large Language Models: Enhancing LLM Performance with Semantic Hints](https://mlsanity.com/paper/2409.14469/) (2024)
- [A Named Entity Recognition and Topic Modeling-based Solution for Locating and Better Assessment of Natural Disasters in Social Media](https://mlsanity.com/paper/2405.00903/) (2024)
- [Disaster Tweets Classification using BERT-Based Language Model](https://mlsanity.com/paper/2202.00795/) (2022)

## Abstract (arXiv)

Urgent blood donation seeking posts and messages on social media often go unnoticed due to the overwhelming volume of daily communications. Traditional app-based systems, reliant on manual input, struggle to reach users in low-resource settings, delaying critical responses. To address this, we introduce the Cognitive Blood Request System (CBRS), a multi-platform framework that efficiently filters and parses blood donation requests from social media streams using a cost-efficient dual-layered architecture. To do so, we curate a novel dataset of 11K parsed blood donation request messages in Bengali, English, and transliterated Bengali, capturing the linguistic diversity of real social media communications. The inclusion of adversarial negatives further enhances the robustness of our model. CBRS achieves an impressive 99% accuracy and precision in filtering, surpassing benchmark methods. In the parsing task, our LoRA finetuned Llama-3.2-3B model achieves 92% zero-shot accuracy, surpassing the base model by 41.54% and exceeding the few-shot performance of GPT-4o-mini, Gemini-2.0-Flash, and other LLMs, while resulting in a 35X reduction in input token usage. This work lays a robust foundation for scalable, inclusive information extraction in time-sensitive, object-focused tasks. Our code, dataset, and trained models are publicly available at [https://github.com/aaniksahaa/CBRS](https://github.com/aaniksahaa/CBRS).