Volunteer-powered automatic classification of social media messages for public health in AIDR

Muhammad Imran, Carlos Castillo

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

4 Citations (Scopus)

Abstract

Microblogging platforms such as Twitter have become a valuable resource for disease surveillance and monitoring. Automatic classification can be used to detect disease-related messages and to sort them into meaningful categories. In this paper, we show how the AIDR (Artificial Intelligence for Disaster Response) platform can be used to harvest and perform analysis of tweets in real-time using supervised ma- chine learning techniques. AIDR is a volunteer-powered on- line social media content classification platform that auto- matically learns from a set of human-annotated examples to classify tweets into user-defined categories. In addition, it automatically increases classification accuracy as new ex- Amples become available. AIDR can be operated through a web interface without the need to deal with the complexity of the machine learning methods used.

Original languageEnglish
Title of host publicationWWW 2014 Companion - Proceedings of the 23rd International Conference on World Wide Web
PublisherAssociation for Computing Machinery, Inc
Pages671-672
Number of pages2
ISBN (Electronic)9781450327459
DOIs
Publication statusPublished - 7 Apr 2014
Event23rd International Conference on World Wide Web, WWW 2014 - Seoul, Korea, Republic of
Duration: 7 Apr 201411 Apr 2014

Other

Other23rd International Conference on World Wide Web, WWW 2014
CountryKorea, Republic of
CitySeoul
Period7/4/1411/4/14

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Keywords

  • Classification
  • Crowdsourcing
  • Epidemics
  • Stream processing

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Software

Cite this

Imran, M., & Castillo, C. (2014). Volunteer-powered automatic classification of social media messages for public health in AIDR. In WWW 2014 Companion - Proceedings of the 23rd International Conference on World Wide Web (pp. 671-672). Association for Computing Machinery, Inc. https://doi.org/10.1145/2567948.2579279