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WSDM 2017 workshop on mining online health reports WSDM workshop summary

Collier, N; Limsopatham, N; Culotta, A; Conway, M; Cox, IJ; Lampos, V; (2017) WSDM 2017 workshop on mining online health reports WSDM workshop summary. In: de Rijke, M and Shokouhi, M and Tomkins, A and Zhang, M, (eds.) WSDM '17: Proceedings of the Tenth ACM International Conference on Web Search and Data Mining. (pp. pp. 825-826). ACM: New York, USA. Green open access

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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 generated through the various facets of Web activity. 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 Information Retrieval and Natural Language Processing. The incorporation of such data and techniques in a number of health-oriented applications has provided strong evidence about the potential benefits, which include better population coverage, timeliness and the operational ability in places with less established health infrastructure. The workshop aims to create a platform where relevant state-of-the-art research is presented, but at the same time discussions among researchers with cross-disciplinary backgrounds can take place. It will focus on the characterisation of data sources, the essential methods for mining this textual information, as well as potential real-world applications and the arising ethical issues. MOHRS '17 will feature 3 keynote talks and 4 accepted paper presentations, together with a panel discussion session.

Type: Proceedings paper
Title: WSDM 2017 workshop on mining online health reports WSDM workshop summary
Event: WSDM '17, Tenth ACM International Conference on Web Search and Data Mining, 6-10 February 2017, Cambridge, UK
ISBN-13: 9781450346757
Open access status: An open access version is available from UCL Discovery
DOI: 10.1145/3018661.3022761
Publisher version: http://dx.doi.org/10.1145/3018661.3022761
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Natural Language Processing; Machine Learning; Compu- tational Health; User-Generated Content
UCL classification: UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/1550379
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