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2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) (2014)
China
Aug. 17, 2014 to Aug. 20, 2014
ISBN: 978-1-4799-5877-1
pp: 349-356
Adham Beykikhoshk , Centre for Pattern Recognition and Data Analytics, Deakin Univeristy, Australia
Ognjen Arandjelovic , Centre for Pattern Recognition and Data Analytics, Deakin Univeristy, Australia
Dinh Phung , Centre for Pattern Recognition and Data Analytics, Deakin Univeristy, Australia
Svetha Venkatesh , Centre for Pattern Recognition and Data Analytics, Deakin Univeristy, Australia
Terry Caelli , National ICT Australia (NICTA), Melbourne, Australia
ABSTRACT
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.
INDEX TERMS
Variable speed drives, Twitter, Autism, Communities, Data mining, Pragmatics, Conferences
CITATION

A. Beykikhoshk, O. Arandjelovic, D. Phung, S. Venkatesh and T. Caelli, "Data-mining twitter and the autism spectrum disorder: A Pilot study," 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), China, 2014, pp. 349-356.
doi:10.1109/ASONAM.2014.6921609
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