Data-mining twitter and the autism spectrum disorder: a pilot study Beykikhoshk,A, Arandjelović,O, Phung,D, Venkatesh,S and Caelli,T 2014, Data-mining twitter and the autism spectrum disorder: a pilot study, in ASONAM 2014 : Proceedings of the 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, IEEE, Piscataway, N.J., pp. 349-356, doi: 10.1109/ASONAM.2014.6921609. Attached Files Name Description MIMEType Size Downloads Title Data-mining twitter and the autism spectrum disorder: a pilot study Author(s) Beykikhoshk,A Arandjelović,O Phung,D orcid.org/0000-0002-9977-8247 Venkatesh,S orcid.org/0000-0001-8675-6631 Caelli,T Conference name Advances in Social Networks Analysis and Mining. Conference (2014: Beijing, China) Conference location Beijing, China Conference dates 17-20 Aug. 2014 Title of proceedings ASONAM 2014 : Proceedings of the 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining Editor(s) [Unknown] Publication date 2014 Conference series Advances in Social Networks Analysis and Mining Conference Start page 349 End page 356 Total pages 8 Publisher IEEE Place of publication Piscataway, N.J. Summary The autism spectrum disorder (ASD) is increasingly being recognized as a major public health issue which affects approximately 0.5-0.6% of the population. Promoting the general awareness of the disorder, increasing the engagement with the affected individuals and their carers, and understanding the success of penetration of the current clinical recommendations in the target communities, is crucial in driving research as well as policy. The aim of the present work is to investigate if Twitter, as a highly popular platform for information exchange, can be used as a data-mining source which could aid in the aforementioned challenges. Specifically, using a large data set of harvested tweets, we present a series of experiments which examine a range of linguistic and semantic aspects of messages posted by individuals interested in ASD. Our findings, the first of their nature in the published scientific literature, strongly motivate additional research on this topic and present a methodological basis for further work. ISBN 9781479958771 Language eng DOI 10.1109/ASONAM.2014.6921609 Field of Research 080109 Pattern Recognition and Data Mining Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences HERDC Research category E1 Full written paper - refereed ERA Research output type E Conference publication Copyright notice ©2014, IEEE Persistent URL http://hdl.handle.net/10536/DRO/DU:30072314 Document type: Conference Paper Collections: Centre for Pattern Recognition and Data Analytics 2018 ERA Submission Related Links Link Description Connect to published version Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Wed, 15 Apr 2015, 12:42:44 EST Wed, 15 Apr 2015, 12:47:33 EST Thu, 16 Apr 2015, 05:12:37 EST Thu, 16 Apr 2015, 06:12:09 EST Mon, 20 Apr 2015, 14:25:26 EST Wed, 27 May 2015, 15:20:50 EST Mon, 18 Jul 2016, 15:35:03 EST Filtered Full Citation counts:   Cited 17 times in TR Web of Science Cited 23 times in Scopus Search Google Scholar Access Statistics: 384 Abstract Views, 6 File Downloads  -  Detailed Statistics Created: Wed, 15 Apr 2015, 12:47:33 EST

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