Mining social media streams to improve public health allergy surveillance

Kathy Lee; Ankit Agrawal; Alok Choudhary

doi:10.1145/2808797.2808896

Mining social media streams to improve public health allergy surveillance

Kathy Lee, Ankit Agrawal, Alok Choudhary

Electrical and Computer Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

41 Scopus citations

Abstract

Allergies are one of the most common chronic diseases worldwide. One in five Americans suffer from either allergy or asthma symptoms. With the prevalence of social media, people sharing experiences and opinions on personal health symptoms and concerns on social media are increasing. Mining those publicly available health related data potentially provides valuable healthcare insights. In this paper, we propose a real-time allergy surveillance system that first classifies tweets to identify those that mention actual allergy incidents using bag-of-words model and NaiveBayesMultinomial classifier and applies in-depth text and spatiotemporal analysis. Our experimental results show that the proposed system can detect predominant allergy types with high precision and that allergy-related tweet volume is highly correlated to the weather data (daily maximum temperature). We believe that this is the first study that examines a large-scale social media stream for in-depth analysis of allergy activities.

Original language	English (US)
Title of host publication	Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015
Editors	Jian Pei, Jie Tang, Fabrizio Silvestri
Publisher	Association for Computing Machinery, Inc
Pages	815-822
Number of pages	8
ISBN (Electronic)	9781450338547
DOIs	https://doi.org/10.1145/2808797.2808896
State	Published - Aug 25 2015
Event	IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015 - Paris, France Duration: Aug 25 2015 → Aug 28 2015

Publication series

Name	Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015

Other

Other	IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015
Country/Territory	France
City	Paris
Period	8/25/15 → 8/28/15

Keywords

Allergy
Public health
Social media
Twitter

ASJC Scopus subject areas

Computer Science Applications
Computer Networks and Communications

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1145/2808797.2808896

Cite this

Lee, K., Agrawal, A., & Choudhary, A. (2015). Mining social media streams to improve public health allergy surveillance. In J. Pei, J. Tang, & F. Silvestri (Eds.), Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015 (pp. 815-822). (Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015). Association for Computing Machinery, Inc. https://doi.org/10.1145/2808797.2808896

Lee, Kathy ; Agrawal, Ankit ; Choudhary, Alok. / Mining social media streams to improve public health allergy surveillance. Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015. editor / Jian Pei ; Jie Tang ; Fabrizio Silvestri. Association for Computing Machinery, Inc, 2015. pp. 815-822 (Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015).

@inproceedings{b385b8d804504ba38bf51958f2a80e16,

title = "Mining social media streams to improve public health allergy surveillance",

abstract = "Allergies are one of the most common chronic diseases worldwide. One in five Americans suffer from either allergy or asthma symptoms. With the prevalence of social media, people sharing experiences and opinions on personal health symptoms and concerns on social media are increasing. Mining those publicly available health related data potentially provides valuable healthcare insights. In this paper, we propose a real-time allergy surveillance system that first classifies tweets to identify those that mention actual allergy incidents using bag-of-words model and NaiveBayesMultinomial classifier and applies in-depth text and spatiotemporal analysis. Our experimental results show that the proposed system can detect predominant allergy types with high precision and that allergy-related tweet volume is highly correlated to the weather data (daily maximum temperature). We believe that this is the first study that examines a large-scale social media stream for in-depth analysis of allergy activities.",

keywords = "Allergy, Public health, Social media, Twitter",

author = "Kathy Lee and Ankit Agrawal and Alok Choudhary",

note = "Funding Information: ACKNOWLEDGMENT This work is supported in part by the following grants: NSF awards CCF-1029166, IIS-1343639, and CCF-1409601. Publisher Copyright: {\textcopyright} 2015 ACM.; IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015 ; Conference date: 25-08-2015 Through 28-08-2015",

year = "2015",

month = aug,

day = "25",

doi = "10.1145/2808797.2808896",

language = "English (US)",

series = "Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015",

publisher = "Association for Computing Machinery, Inc",

pages = "815--822",

editor = "Jian Pei and Jie Tang and Fabrizio Silvestri",

booktitle = "Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015",

}

Lee, K, Agrawal, A & Choudhary, A 2015, Mining social media streams to improve public health allergy surveillance. in J Pei, J Tang & F Silvestri (eds), Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015. Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015, Association for Computing Machinery, Inc, pp. 815-822, IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015, Paris, France, 8/25/15. https://doi.org/10.1145/2808797.2808896

Mining social media streams to improve public health allergy surveillance. / Lee, Kathy; Agrawal, Ankit ; Choudhary, Alok.
Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015. ed. / Jian Pei; Jie Tang; Fabrizio Silvestri. Association for Computing Machinery, Inc, 2015. p. 815-822 (Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Mining social media streams to improve public health allergy surveillance

AU - Lee, Kathy

AU - Agrawal, Ankit

AU - Choudhary, Alok

PY - 2015/8/25

Y1 - 2015/8/25

N2 - Allergies are one of the most common chronic diseases worldwide. One in five Americans suffer from either allergy or asthma symptoms. With the prevalence of social media, people sharing experiences and opinions on personal health symptoms and concerns on social media are increasing. Mining those publicly available health related data potentially provides valuable healthcare insights. In this paper, we propose a real-time allergy surveillance system that first classifies tweets to identify those that mention actual allergy incidents using bag-of-words model and NaiveBayesMultinomial classifier and applies in-depth text and spatiotemporal analysis. Our experimental results show that the proposed system can detect predominant allergy types with high precision and that allergy-related tweet volume is highly correlated to the weather data (daily maximum temperature). We believe that this is the first study that examines a large-scale social media stream for in-depth analysis of allergy activities.

AB - Allergies are one of the most common chronic diseases worldwide. One in five Americans suffer from either allergy or asthma symptoms. With the prevalence of social media, people sharing experiences and opinions on personal health symptoms and concerns on social media are increasing. Mining those publicly available health related data potentially provides valuable healthcare insights. In this paper, we propose a real-time allergy surveillance system that first classifies tweets to identify those that mention actual allergy incidents using bag-of-words model and NaiveBayesMultinomial classifier and applies in-depth text and spatiotemporal analysis. Our experimental results show that the proposed system can detect predominant allergy types with high precision and that allergy-related tweet volume is highly correlated to the weather data (daily maximum temperature). We believe that this is the first study that examines a large-scale social media stream for in-depth analysis of allergy activities.

KW - Allergy

KW - Public health

KW - Social media

KW - Twitter

UR - http://www.scopus.com/inward/record.url?scp=84962504309&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84962504309&partnerID=8YFLogxK

U2 - 10.1145/2808797.2808896

DO - 10.1145/2808797.2808896

M3 - Conference contribution

AN - SCOPUS:84962504309

T3 - Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015

SP - 815

EP - 822

BT - Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015

A2 - Pei, Jian

A2 - Tang, Jie

A2 - Silvestri, Fabrizio

PB - Association for Computing Machinery, Inc

T2 - IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015

Y2 - 25 August 2015 through 28 August 2015

ER -

Lee K, Agrawal A , Choudhary A. Mining social media streams to improve public health allergy surveillance. In Pei J, Tang J, Silvestri F, editors, Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015. Association for Computing Machinery, Inc. 2015. p. 815-822. (Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015). doi: 10.1145/2808797.2808896

Mining social media streams to improve public health allergy surveillance

Abstract

Publication series

Other

Keywords

ASJC Scopus subject areas

UN SDGs

Access to Document

Other files and links

Fingerprint

Cite this