WSDM 2017 Workshop on Mining Online Health Reports: WSDM workshop summary

Nigel Collier, Nut Limsopatham, Aron Culotta, Mike Conway, Ingemar Johansson Cox, Vasileios Lampos

Abstract

The workshop on Mining Online Health Reports (MOHRS) draws upon the rapidly developing field of Computational Health, focusing on textual content that has been gener- ated through various activities on the Web. Online user- generated information mining, especially from social media platforms and search engines, has been in the forefront of many research efforts, especially in the fields of Informa- tion Retrieval and Natural Language Processing. The in- corporation of such data and techniques in a number of health-oriented applications has provided strong evidence of the potential benefits, which include better population coverage, timeliness and applicability to places with less established health infrastructure. The workshop provides an opportunity to present relevant state-of-the-art research, and a venue for discussion between researchers with cross- disciplinary backgrounds. It will focus on the characterisa- tion of data sources, the essential methods for mining this textual information, as well as potential real-world applica- tions and the arising ethical issues. MOHRS '17 will feature 3 keynote talks and 4 accepted paper presentations, as well as a panel discussion.

Original languageEnglish
Title of host publicationProceedings of the Tenth ACM International Conference on Web Search and Data Mining
Number of pages2
PublisherAssociation for Computing Machinery
Publication date2 Feb 2017
Pages825-826
ISBN (Electronic)978-1-4503-4675-7
DOIs
Publication statusPublished - 2 Feb 2017
Event10th ACM International Conference on Web Search and Data Mining - Cambridge, United Kingdom
Duration: 6 Feb 201710 Feb 2017
Conference number: 10

Conference

Conference10th ACM International Conference on Web Search and Data Mining
Number10
Country/TerritoryUnited Kingdom
CityCambridge
Period06/02/201710/02/2017

Keywords

  • Compu-tational health
  • Machine learning
  • Natural language processing
  • User-generated content

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