Twitter Catches the Flu: Detecting Influenza Epidemics Using Twitter
E. Aramaki, S. Maskawa, and M. Morita. Proceedings of the Conference on Empirical Methods in Natural Language Processing, page 1568--1576. Stroudsburg, PA, USA, Association for Computational Linguistics, (2011)
Abstract
With the recent rise in popularity and scale of social media, a growing need exists for systems that can extract useful information from huge amounts of data. We address the issue of detecting influenza epidemics. First, the proposed system extracts influenza related tweets using Twitter API. Then, only tweets that mention actual influenza patients are extracted by the support vector machine (SVM) based classifier. The experiment results demonstrate the feasibility of the proposed approach (0.89 correlation to the gold standard). Especially at the outbreak and early spread (early epidemic stage), the proposed method shows high correlation (0.97 correlation), which outperforms the state-of-the-art methods. This paper describes that Twitter texts reflect the real world, and that NLP techniques can be applied to extract only tweets that contain useful information.
%0 Conference Paper
%1 Aramaki:2011:TCF:2145432.2145600
%A Aramaki, Eiji
%A Maskawa, Sachiko
%A Morita, Mizuki
%B Proceedings of the Conference on Empirical Methods in Natural Language Processing
%C Stroudsburg, PA, USA
%D 2011
%I Association for Computational Linguistics
%K flu influenza m1 twitter
%P 1568--1576
%T Twitter Catches the Flu: Detecting Influenza Epidemics Using Twitter
%U http://dl.acm.org/citation.cfm?id=2145432.2145600
%X With the recent rise in popularity and scale of social media, a growing need exists for systems that can extract useful information from huge amounts of data. We address the issue of detecting influenza epidemics. First, the proposed system extracts influenza related tweets using Twitter API. Then, only tweets that mention actual influenza patients are extracted by the support vector machine (SVM) based classifier. The experiment results demonstrate the feasibility of the proposed approach (0.89 correlation to the gold standard). Especially at the outbreak and early spread (early epidemic stage), the proposed method shows high correlation (0.97 correlation), which outperforms the state-of-the-art methods. This paper describes that Twitter texts reflect the real world, and that NLP techniques can be applied to extract only tweets that contain useful information.
%@ 978-1-937284-11-4
@inproceedings{Aramaki:2011:TCF:2145432.2145600,
abstract = {With the recent rise in popularity and scale of social media, a growing need exists for systems that can extract useful information from huge amounts of data. We address the issue of detecting influenza epidemics. First, the proposed system extracts influenza related tweets using Twitter API. Then, only tweets that mention actual influenza patients are extracted by the support vector machine (SVM) based classifier. The experiment results demonstrate the feasibility of the proposed approach (0.89 correlation to the gold standard). Especially at the outbreak and early spread (early epidemic stage), the proposed method shows high correlation (0.97 correlation), which outperforms the state-of-the-art methods. This paper describes that Twitter texts reflect the real world, and that NLP techniques can be applied to extract only tweets that contain useful information.},
acmid = {2145600},
added-at = {2016-06-21T11:33:59.000+0200},
address = {Stroudsburg, PA, USA},
author = {Aramaki, Eiji and Maskawa, Sachiko and Morita, Mizuki},
biburl = {https://www.bibsonomy.org/bibtex/2701ae12db677ba728a8472293de989db/asmelash},
booktitle = {Proceedings of the Conference on Empirical Methods in Natural Language Processing},
description = {Twitter catches the flu},
interhash = {5ecd517f2a385330c6ab900c3403d689},
intrahash = {701ae12db677ba728a8472293de989db},
isbn = {978-1-937284-11-4},
keywords = {flu influenza m1 twitter},
location = {Edinburgh, United Kingdom},
numpages = {9},
pages = {1568--1576},
publisher = {Association for Computational Linguistics},
series = {EMNLP '11},
timestamp = {2016-06-21T11:33:59.000+0200},
title = {Twitter Catches the Flu: Detecting Influenza Epidemics Using Twitter},
url = {http://dl.acm.org/citation.cfm?id=2145432.2145600},
year = 2011
}