Using Twitter to learn about the autism community
arXiv:1506.00246 · doi:10.1007/s13278-015-0261-5
Abstract
Considering the raising socio-economic burden of autism spectrum disorder (ASD), timely and evidence-driven public policy decision making and communication of the latest guidelines pertaining to the treatment and management of the disorder is crucial. Yet evidence suggests that policy makers and medical practitioners do not always have a good understanding of the practices and relevant beliefs of ASD-afflicted individuals' carers who often follow questionable recommendations and adopt advice poorly supported by scientific data. The key goal of the present work is to explore the idea that Twitter, as a highly popular platform for information exchange, could be used as a data-mining source to learn about the population affected by ASD -- their behaviour, concerns, needs etc. To this end, using a large data set of over 11 million harvested tweets as the basis for our investigation, we describe 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.
Social Network Analysis and Mining, 2015
References in corpus (4)
- Power-law distributions in empirical data
- The Geography of Happiness: Connecting Twitter sentiment and expression, demographics, and objective characteristics of place
- Early Stage Influenza Detection from Twitter
- Hierarchical Dirichlet process for tracking complex topical structure evolution and its application to autism research literature
Cited by in corpus (4)
- Overcoming data scarcity of Twitter: using tweets as bootstrap with application to autism-related topic content analysis
- Prediction of future hospital admissions - what is the tradeoff between specificity and accuracy?
- Twitter and disability activism: leadership and relevant topics in the online conversation
- Discovering topic structures of a temporally evolving document corpus