A hierarchical learning model for extracting public health data from social media

Elahm Rastegari, Hesham H Ali, Sasan Azizian

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

In decision-making processes, particularly in the healthcare domain, each relevant piece of information is important. This is particularly important when it comes to the health conditions for them there remains a high degree of non-determinism regarding treatment approaches. Online social media are places in which people feel free to share their opinions about numerous topics, including public health issues and how individuals have perceived the efficacy of different types of treatments associated with diseases. social media could represent a secondary source that can be used as a supplement to other data sources. This would allow individuals as well as healthcare providers to gain insight related to public health from different angels. In this study, we construct a hierarchical learning model based on Twitter data that can extract valuable knowledge associated with public health. Back pain was selected for our case study to demonstrate how the proposed model works.

Original languageEnglish (US)
Title of host publicationAMCIS 2017 - America's Conference on Information Systems
Subtitle of host publicationA Tradition of Innovation
PublisherAmericas Conference on Information Systems
Volume2017-August
ISBN (Electronic)9780996683142
StatePublished - Jan 1 2017
EventAmerica�s Conference on Information Systems: A Tradition of Innovation, AMCIS 2017 - Boston, United States
Duration: Aug 10 2017Aug 12 2017

Other

OtherAmerica�s Conference on Information Systems: A Tradition of Innovation, AMCIS 2017
CountryUnited States
CityBoston
Period8/10/178/12/17

Fingerprint

Public health
Decision making
Health

Keywords

  • Public health
  • Sentiment Analysis
  • Twitter

ASJC Scopus subject areas

  • Computer Science Applications
  • Information Systems
  • Computer Networks and Communications

Cite this

Rastegari, E., Ali, H. H., & Azizian, S. (2017). A hierarchical learning model for extracting public health data from social media. In AMCIS 2017 - America's Conference on Information Systems: A Tradition of Innovation (Vol. 2017-August). Americas Conference on Information Systems.

A hierarchical learning model for extracting public health data from social media. / Rastegari, Elahm; Ali, Hesham H; Azizian, Sasan.

AMCIS 2017 - America's Conference on Information Systems: A Tradition of Innovation. Vol. 2017-August Americas Conference on Information Systems, 2017.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Rastegari, E, Ali, HH & Azizian, S 2017, A hierarchical learning model for extracting public health data from social media. in AMCIS 2017 - America's Conference on Information Systems: A Tradition of Innovation. vol. 2017-August, Americas Conference on Information Systems, America�s Conference on Information Systems: A Tradition of Innovation, AMCIS 2017, Boston, United States, 8/10/17.
Rastegari E, Ali HH, Azizian S. A hierarchical learning model for extracting public health data from social media. In AMCIS 2017 - America's Conference on Information Systems: A Tradition of Innovation. Vol. 2017-August. Americas Conference on Information Systems. 2017
Rastegari, Elahm ; Ali, Hesham H ; Azizian, Sasan. / A hierarchical learning model for extracting public health data from social media. AMCIS 2017 - America's Conference on Information Systems: A Tradition of Innovation. Vol. 2017-August Americas Conference on Information Systems, 2017.
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