research-article

Making use of social media data in public health

Authors:

Kerstin Denecke,

Peter Dolog,

Pavel SmrzAuthors Info & Claims

WWW '12 Companion: Proceedings of the 21st International Conference on World Wide Web

Pages 243 - 246

https://doi.org/10.1145/2187980.2188019

Published: 16 April 2012 Publication History

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Abstract

Disease surveillance systems exist to offer an easily accessible "epidemiological snapshot" on up-to-date summary statistics for numerous infectious diseases. However, these indicator-based systems represent only part of the solution. Experiences show that they fail when confronted with agents that are new emerging like the agents causing the lung disease SARS in 2002. Further, due to slow reporting mechanisms, the time until health threats become visible to public health officials can be long. The M-Eco project provides an event-based approach to the early detection of emerging health threats. The developed technologies exploit content from social media and multimedia data as input and analyze it by sophisticated event-detection techniques to identify potential threats. Alerts for public health threats are provided to the user in a personalized way.

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Cited By

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Nguyen HLe H(2023)Predicting Community Health Through Heterogeneous Social NetworksSN Computer Science10.1007/s42979-023-01718-z4:3Online publication date: 21-Feb-2023
https://doi.org/10.1007/s42979-023-01718-z
Pater JFiesler CZimmer M(2022)No Humans HereProceedings of the ACM on Human-Computer Interaction10.1145/34928576:GROUP(1-13)Online publication date: 14-Jan-2022
https://dl.acm.org/doi/10.1145/3492857
Le HNguyen H(2020)On the Use of Textual and Visual Data from Online Social Networks for Predicting Community Health2020 International Conference on Advanced Computing and Applications (ACOMP)10.1109/ACOMP50827.2020.00016(55-62)Online publication date: Nov-2020
https://doi.org/10.1109/ACOMP50827.2020.00016
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Index Terms

Making use of social media data in public health
1. Applied computing
  1. Life and medical sciences
2. Information systems
  1. Information retrieval

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Published In

WWW '12 Companion: Proceedings of the 21st International Conference on World Wide Web

April 2012

1250 pages

ISBN:9781450312301

DOI:10.1145/2187980

General Chairs:
Alain Mille
Université de Lyon, France
,
Fabien Gandon
INRIA, France
,
Jacques Misselis
HP, France
,
Program Chairs:
Michael Rabinovich
Case Western Reserve University, USA
,
Steffen Staab
University of Koblenz-Landau, Germany

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 16 April 2012

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WWW 2012

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Univ. de Lyon

WWW 2012: 21st World Wide Web Conference 2012

April 16 - 20, 2012

Lyon, France

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Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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Nguyen HLe H(2023)Predicting Community Health Through Heterogeneous Social NetworksSN Computer Science10.1007/s42979-023-01718-z4:3Online publication date: 21-Feb-2023
https://doi.org/10.1007/s42979-023-01718-z
Pater JFiesler CZimmer M(2022)No Humans HereProceedings of the ACM on Human-Computer Interaction10.1145/34928576:GROUP(1-13)Online publication date: 14-Jan-2022
https://dl.acm.org/doi/10.1145/3492857
Le HNguyen H(2020)On the Use of Textual and Visual Data from Online Social Networks for Predicting Community Health2020 International Conference on Advanced Computing and Applications (ACOMP)10.1109/ACOMP50827.2020.00016(55-62)Online publication date: Nov-2020
https://doi.org/10.1109/ACOMP50827.2020.00016
Gupta AKatarya R(2020)Social Media based Surveillance Systems for Healthcare using Machine Learning: A Systematic ReviewJournal of Biomedical Informatics10.1016/j.jbi.2020.103500(103500)Online publication date: Jul-2020
https://doi.org/10.1016/j.jbi.2020.103500
Alruwaili WProtano BSirigiriraju TAlhoori H(2019)Using Arabic Tweets to Understand Drug Selling BehaviorsProcedia Computer Science10.1016/j.procs.2019.12.220164(559-564)Online publication date: 2019
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van de Belt Tvan Stockum PEngelen LLancee JSchrijver RRodríguez-Baño JTacconelli ESaris Kvan Gelder MVoss A(2018)Social media posts and online search behaviour as early-warning system for MRSA outbreaksAntimicrobial Resistance & Infection Control10.1186/s13756-018-0359-47:1Online publication date: 30-May-2018
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Chao HCao YZhang JXia FZhou YShan H(2018)Population Density-Based Hospital Recommendation with Mobile LBS Big Data2018 IEEE International Conference on Big Data and Smart Computing (BigComp)10.1109/BigComp.2018.00015(37-44)Online publication date: Jan-2018
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Alimova ISolovyev V(2018)Interactive Attention Network for Adverse Drug Reaction ClassificationArtificial Intelligence and Natural Language10.1007/978-3-030-01204-5_18(185-196)Online publication date: 27-Sep-2018
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Ghosh SChakraborty PNsoesie ECohn EMekaru SBrownstein JRamakrishnan N(2017)Temporal Topic Modeling to Assess Associations between News Trends and Infectious Disease OutbreaksScientific Reports10.1038/srep408417:1Online publication date: 19-Jan-2017
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El-Bathy NGloster CAzar GEl-Bathy MStein GStevenson R(2014)Intelligent surveillance lifecycle architecture for epidemiological data clustering using Twitter and novel genetic algorithmIEEE International Conference on Electro/Information Technology10.1109/EIT.2014.6871753(149-155)Online publication date: Jun-2014
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Recommendations

Mining Social Media Streams to Improve Public Health Allergy Surveillance

Public health and social media: A study of Zika virus‐related posts on Yahoo! Answers

Surveillance of methadone-related adverse drug events using multiple public health data sources

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