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WSDM 2017 Workshop on Mining Online Health Reports

TitleWSDM 2017 Workshop on Mining Online Health Reports
Publication TypeConference Paper
Year of Publication2017
AuthorsCollier, N, Limsopatham, N, Culotta, A, Conway, M, Cox, IJ, Lampos, V
Conference NameProceedings of the 10th ACM International Conference on Web Search and Data Mining
Pagination825-826
Date Published02/2017
PublisherACM
KeywordsComputational Health, Machine Learning, Natural Language Processing, user-generated content
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 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 Information Retrieval and Natural Language Processing. The incorporation 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 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, as well as a panel discussion.

DOI10.1145/3018661.3022761
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BibTeX

@inproceedings{mohrs2017,
author = {Collier, Nigel and Limsopatham, Nut and Culotta, Aron and Conway, Mike and Cox, Ingemar J. and Lampos, Vasileios},
title = {{WSDM 2017 Workshop on Mining Online Health Reports}},
booktitle = {Proceedings of the 10th ACM International Conference on Web Search and Data Mining},
series = {WSDM '17},
pages = {825--826},
year = {2017},
doi = {10.1145/3018661.3022761}
}