@inproceedings{mathur-etal-2018-identification,
title = "Identification of Emergency Blood Donation Request on {T}witter",
author = "Mathur, Puneet and
Ayyar, Meghna and
Chopra, Sahil and
Shahid, Simra and
Mehnaz, Laiba and
Shah, Rajiv",
editor = "Gonzalez-Hernandez, Graciela and
Weissenbacher, Davy and
Sarker, Abeed and
Paul, Michael",
booktitle = "Proceedings of the 2018 {EMNLP} Workshop {SMM}4{H}: The 3rd Social Media Mining for Health Applications Workshop {\&} Shared Task",
month = oct,
year = "2018",
address = "Brussels, Belgium",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W18-5907",
doi = "10.18653/v1/W18-5907",
pages = "27--31",
abstract = "Social media-based text mining in healthcare has received special attention in recent times due to the enhanced accessibility of social media sites like Twitter. The increasing trend of spreading important information in distress can help patients reach out to prospective blood donors in a time bound manner. However such manual efforts are mostly inefficient due to the limited network of a user. In a novel step to solve this problem, we present an annotated Emergency Blood Donation Request (EBDR) dataset to classify tweets referring to the necessity of urgent blood donation requirement. Additionally, we also present an automated feature-based SVM classification technique that can help selective EBDR tweets reach relevant personals as well as medical authorities. Our experiments also present a quantitative evidence that linguistic along with handcrafted heuristics can act as the most representative set of signals this task with an accuracy of 97.89{\%}.",
}
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%0 Conference Proceedings
%T Identification of Emergency Blood Donation Request on Twitter
%A Mathur, Puneet
%A Ayyar, Meghna
%A Chopra, Sahil
%A Shahid, Simra
%A Mehnaz, Laiba
%A Shah, Rajiv
%Y Gonzalez-Hernandez, Graciela
%Y Weissenbacher, Davy
%Y Sarker, Abeed
%Y Paul, Michael
%S Proceedings of the 2018 EMNLP Workshop SMM4H: The 3rd Social Media Mining for Health Applications Workshop & Shared Task
%D 2018
%8 October
%I Association for Computational Linguistics
%C Brussels, Belgium
%F mathur-etal-2018-identification
%X Social media-based text mining in healthcare has received special attention in recent times due to the enhanced accessibility of social media sites like Twitter. The increasing trend of spreading important information in distress can help patients reach out to prospective blood donors in a time bound manner. However such manual efforts are mostly inefficient due to the limited network of a user. In a novel step to solve this problem, we present an annotated Emergency Blood Donation Request (EBDR) dataset to classify tweets referring to the necessity of urgent blood donation requirement. Additionally, we also present an automated feature-based SVM classification technique that can help selective EBDR tweets reach relevant personals as well as medical authorities. Our experiments also present a quantitative evidence that linguistic along with handcrafted heuristics can act as the most representative set of signals this task with an accuracy of 97.89%.
%R 10.18653/v1/W18-5907
%U https://aclanthology.org/W18-5907
%U https://doi.org/10.18653/v1/W18-5907
%P 27-31
Markdown (Informal)
[Identification of Emergency Blood Donation Request on Twitter](https://aclanthology.org/W18-5907) (Mathur et al., EMNLP 2018)
ACL
- Puneet Mathur, Meghna Ayyar, Sahil Chopra, Simra Shahid, Laiba Mehnaz, and Rajiv Shah. 2018. Identification of Emergency Blood Donation Request on Twitter. In Proceedings of the 2018 EMNLP Workshop SMM4H: The 3rd Social Media Mining for Health Applications Workshop & Shared Task, pages 27–31, Brussels, Belgium. Association for Computational Linguistics.