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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.

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Title Data-mining twitter and the autism spectrum disorder: a pilot study
Author(s) Beykikhoshk,A
Arandjelović,O
Phung,DORCID iD for Phung,D orcid.org/0000-0002-9977-8247
Venkatesh,SORCID iD for 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
Collection: Centre for Pattern Recognition and Data Analytics
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